{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup\n",
    "import time\n",
    "import os\n",
    "\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load house records into dataframe\n",
    "data_column = ['Car_No', 'Car_Price',  'Car_ORC', 'Car_Title', 'Car_Year', 'Car_Brand', 'Car_Model','Car_Location','Car_Mileage(km)',  'Car_Type', 'Car_Gear', 'Car_Seat', 'Car_Fuel(cc)',  'Car_Door_Num',  'Car_Wof_Exp', 'Car_Fuel_Type', 'Car_Reg', 'Car_Color']\n",
    "Car_data_target = pd.read_csv('car_result_FINAL.txt', sep = '|', dtype = 'str', header=None)\n",
    "Car_data_target.columns = data_column \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13442, 18)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check on duplicate values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Wof_Exp</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Reg</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>89038</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2007 Lexus IS250</td>\n",
       "      <td>2007</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>IS250</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107000</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>85695</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2011 Volkswagen Golf 1.4 90Kw</td>\n",
       "      <td>2011</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>32800</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>May 2021</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5533</th>\n",
       "      <td>75514</td>\n",
       "      <td>8995</td>\n",
       "      <td>0</td>\n",
       "      <td>2004 Nissan Murano Full Leather Nice Car</td>\n",
       "      <td>2004</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Murano</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>138452</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>3498</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Car_No Car_Price Car_ORC                                 Car_Title  \\\n",
       "594   89038      9990       0                          2007 Lexus IS250   \n",
       "1342  85695      9990       0             2011 Volkswagen Golf 1.4 90Kw   \n",
       "5533  75514      8995       0  2004 Nissan Murano Full Leather Nice Car   \n",
       "\n",
       "     Car_Year   Car_Brand Car_Model Car_Location Car_Mileage(km)   Car_Type  \\\n",
       "594      2007       Lexus     IS250     Auckland          107000      Sedan   \n",
       "1342     2011  Volkswagen      Golf     Auckland           32800  Hatchback   \n",
       "5533     2004      Nissan    Murano     Auckland          138452     RV/SUV   \n",
       "\n",
       "       Car_Gear Car_Seat Car_Fuel(cc) Car_Door_Num Car_Wof_Exp Car_Fuel_Type  \\\n",
       "594   Tiptronic      NaN         2500          NaN         NaN        Petrol   \n",
       "1342  Automatic        5         1389          NaN         NaN        Petrol   \n",
       "5533  Automatic        5         3498            5         NaN        Petrol   \n",
       "\n",
       "       Car_Reg Car_Color  \n",
       "594        NaN     White  \n",
       "1342  May 2021       Red  \n",
       "5533       NaN      Gold  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[(Car_data_target.duplicated(['Car_No']) == True)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Wof_Exp</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Reg</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>89038</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2007 Lexus IS250</td>\n",
       "      <td>2007</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>IS250</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107000</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apr 2022</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>89038</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2007 Lexus IS250</td>\n",
       "      <td>2007</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>IS250</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107000</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>85695</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2011 Volkswagen Golf 1.4 90Kw</td>\n",
       "      <td>2011</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>32800</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mar 2022</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>May 2021</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>85695</td>\n",
       "      <td>9990</td>\n",
       "      <td>0</td>\n",
       "      <td>2011 Volkswagen Golf 1.4 90Kw</td>\n",
       "      <td>2011</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>32800</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>May 2021</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5532</th>\n",
       "      <td>75514</td>\n",
       "      <td>8995</td>\n",
       "      <td>0</td>\n",
       "      <td>2004 Nissan Murano Full Leather Nice Car</td>\n",
       "      <td>2004</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Murano</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>138452</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>3498</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5533</th>\n",
       "      <td>75514</td>\n",
       "      <td>8995</td>\n",
       "      <td>0</td>\n",
       "      <td>2004 Nissan Murano Full Leather Nice Car</td>\n",
       "      <td>2004</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Murano</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>138452</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>3498</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Car_No Car_Price Car_ORC                                 Car_Title  \\\n",
       "593   89038      9990       0                          2007 Lexus IS250   \n",
       "594   89038      9990       0                          2007 Lexus IS250   \n",
       "1341  85695      9990       0             2011 Volkswagen Golf 1.4 90Kw   \n",
       "1342  85695      9990       0             2011 Volkswagen Golf 1.4 90Kw   \n",
       "5532  75514      8995       0  2004 Nissan Murano Full Leather Nice Car   \n",
       "5533  75514      8995       0  2004 Nissan Murano Full Leather Nice Car   \n",
       "\n",
       "     Car_Year   Car_Brand Car_Model Car_Location Car_Mileage(km)   Car_Type  \\\n",
       "593      2007       Lexus     IS250     Auckland          107000      Sedan   \n",
       "594      2007       Lexus     IS250     Auckland          107000      Sedan   \n",
       "1341     2011  Volkswagen      Golf     Auckland           32800  Hatchback   \n",
       "1342     2011  Volkswagen      Golf     Auckland           32800  Hatchback   \n",
       "5532     2004      Nissan    Murano     Auckland          138452     RV/SUV   \n",
       "5533     2004      Nissan    Murano     Auckland          138452     RV/SUV   \n",
       "\n",
       "       Car_Gear Car_Seat Car_Fuel(cc) Car_Door_Num Car_Wof_Exp Car_Fuel_Type  \\\n",
       "593   Tiptronic      NaN         2500          NaN    Apr 2022        Petrol   \n",
       "594   Tiptronic      NaN         2500          NaN         NaN        Petrol   \n",
       "1341  Automatic        5         1389          NaN    Mar 2022        Petrol   \n",
       "1342  Automatic        5         1389          NaN         NaN        Petrol   \n",
       "5532  Automatic        5         3498            5         NaN        Petrol   \n",
       "5533  Automatic        5         3498            5         NaN        Petrol   \n",
       "\n",
       "       Car_Reg Car_Color  \n",
       "593        NaN     White  \n",
       "594        NaN     White  \n",
       "1341  May 2021       Red  \n",
       "1342  May 2021       Red  \n",
       "5532       NaN      Gold  \n",
       "5533       NaN      Gold  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[(Car_data_target['Car_No'] == '89038') | (Car_data_target['Car_No'] == '85695') | (Car_data_target['Car_No'] == '75514')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop duplicate records "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target = Car_data_target.drop(Car_data_target[(Car_data_target['Car_No'] == '89038') & (Car_data_target['Car_Wof_Exp'].isnull() == True)].index)\n",
    "Car_data_target = Car_data_target.drop(Car_data_target[(Car_data_target['Car_No'] == '85695') & (Car_data_target['Car_Wof_Exp'].isnull() == True)].index)\n",
    "Car_data_target.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13439, 18)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Wof_Exp</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Reg</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Car_No, Car_Price, Car_ORC, Car_Title, Car_Year, Car_Brand, Car_Model, Car_Location, Car_Mileage(km), Car_Type, Car_Gear, Car_Seat, Car_Fuel(cc), Car_Door_Num, Car_Wof_Exp, Car_Fuel_Type, Car_Reg, Car_Color]\n",
       "Index: []"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[(Car_data_target.duplicated(['Car_No']) == True)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_No                 0\n",
       "Car_Price            863\n",
       "Car_ORC                0\n",
       "Car_Title              0\n",
       "Car_Year               0\n",
       "Car_Brand              0\n",
       "Car_Model             91\n",
       "Car_Location           0\n",
       "Car_Mileage(km)        0\n",
       "Car_Type               1\n",
       "Car_Gear              33\n",
       "Car_Seat            2645\n",
       "Car_Fuel(cc)         725\n",
       "Car_Door_Num        2531\n",
       "Car_Wof_Exp        10634\n",
       "Car_Fuel_Type        496\n",
       "Car_Reg            12889\n",
       "Car_Color           1608\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing value percentage for each columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Reg            0.959074\n",
       "Car_Wof_Exp        0.791279\n",
       "Car_Seat           0.196815\n",
       "Car_Door_Num       0.188332\n",
       "Car_Color          0.119652\n",
       "Car_Price          0.064216\n",
       "Car_Fuel(cc)       0.053947\n",
       "Car_Fuel_Type      0.036908\n",
       "Car_Model          0.006771\n",
       "Car_Gear           0.002456\n",
       "Car_Type           0.000074\n",
       "Car_Title          0.000000\n",
       "Car_ORC            0.000000\n",
       "Car_Mileage(km)    0.000000\n",
       "Car_Year           0.000000\n",
       "Car_Brand          0.000000\n",
       "Car_Location       0.000000\n",
       "Car_No             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target.isnull().sum()/len(Car_data_target))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop columns which missing percentage larger than 50%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop columns which missing percentage larger than 50% \n",
    "# drop Car_Reg and Wof_Exp \n",
    "Car_data_target.drop(['Car_Reg'], axis=1, inplace=True)\n",
    "\n",
    "Car_data_target.drop(['Car_Wof_Exp'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assign Car_Seat and Car_Door_Num according to Car_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      NaN\n",
       "2        5\n",
       "6        4\n",
       "63       2\n",
       "122      3\n",
       "Name: Car_Door_Num, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target['Car_Door_Num'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                5\n",
       "1                7\n",
       "4                8\n",
       "18             NaN\n",
       "31               4\n",
       "42               3\n",
       "46               2\n",
       "425              6\n",
       "1536    10 or more\n",
       "Name: Car_Seat, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target['Car_Seat'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target['Car_Seat'] = Car_data_target['Car_Seat'].replace('10 or more', '10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Car_data_target[(Car_data_target['Car_Seat'] == '10 or more')]\n",
    "\n",
    "Car_data_target['Car_Seat'].fillna(value = 0, inplace=True)\n",
    "Car_data_target['Car_Door_Num'].fillna(value = 0, inplace=True)\n",
    "Car_data_target['Car_Fuel(cc)'].fillna(value = 0, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5\n",
       "1        7\n",
       "4        8\n",
       "18       0\n",
       "31       4\n",
       "42       3\n",
       "46       2\n",
       "425      6\n",
       "1536    10\n",
       "Name: Car_Seat, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target['Car_Seat'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86261</td>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005 Nissan Tiida Axis</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Tiida</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79395</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009 Toyota Wish</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Wish</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7</td>\n",
       "      <td>1800</td>\n",
       "      <td>0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85473</td>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>2400</td>\n",
       "      <td>5</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88342</td>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>2400</td>\n",
       "      <td>5</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72923</td>\n",
       "      <td>9900</td>\n",
       "      <td>1</td>\n",
       "      <td>2012 Nissan Serena 20X</td>\n",
       "      <td>2012</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Serena</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107126</td>\n",
       "      <td>Van</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>8</td>\n",
       "      <td>2000</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Light Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_No Car_Price Car_ORC                                          Car_Title  \\\n",
       "0  86261      3997       0                             2005 Nissan Tiida Axis   \n",
       "1  79395      7997       0                                   2009 Toyota Wish   \n",
       "2  85473     9940        1  2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...   \n",
       "3  88342     8940        1  2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...   \n",
       "4  72923     9900        1                             2012 Nissan Serena 20X   \n",
       "\n",
       "  Car_Year   Car_Brand  Car_Model Car_Location Car_Mileage(km)       Car_Type  \\\n",
       "0     2005      Nissan      Tiida      Dunedin          138150      Hatchback   \n",
       "1     2009      Toyota       Wish      Dunedin          100300  Station Wagon   \n",
       "2     2007  Mitsubishi  OUTLANDER     Auckland          102425         RV/SUV   \n",
       "3     2006  Mitsubishi  OUTLANDER     Auckland          109425         RV/SUV   \n",
       "4     2012      Nissan     Serena     Auckland          107126            Van   \n",
       "\n",
       "    Car_Gear Car_Seat Car_Fuel(cc) Car_Door_Num Car_Fuel_Type   Car_Color  \n",
       "0  Automatic        5         1500            0        Petrol      Silver  \n",
       "1  Automatic        7         1800            0        Petrol       White  \n",
       "2  Automatic        5         2400            5        Petrol      Silver  \n",
       "3  Automatic        5         2400            5        Petrol       Black  \n",
       "4  Automatic        8         2000            5           NaN  Light Blue  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tiida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>TIIDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4145</th>\n",
       "      <td>tiida</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Car_Model\n",
       "0        Tiida\n",
       "75       TIIDA\n",
       "4145     tiida"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target[['Car_Model']][(Car_data_target['Car_Model'].str.upper() == 'TIIDA')].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert data type from object to int\n",
    "Car_data_target['Car_Seat'] = Car_data_target['Car_Seat'].astype(int)\n",
    "\n",
    "Car_data_target['Car_Door_Num'] = Car_data_target['Car_Door_Num'].astype(int)\n",
    "\n",
    "Car_data_target['Car_Fuel(cc)'] = Car_data_target['Car_Fuel(cc)'].astype(int)\n",
    "\n",
    "Car_data_target['Car_Fuel(cc)'] = Car_data_target['Car_Fuel(cc)'].astype(int)\n",
    "\n",
    "\n",
    "Car_data_target['Car_Model_upper'] = Car_data_target['Car_Model'].str.upper()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Get median door number and seat for each brand and model\n",
    "\n",
    "Car_median_seat = Car_data_target[(Car_data_target['Car_Seat'] != 0)].groupby(['Car_Brand', 'Car_Model_upper', 'Car_Type'])[['Car_Seat']].median().reset_index()\n",
    "\n",
    "Car_median_door = Car_data_target[(Car_data_target['Car_Door_Num'] != 0)].groupby(['Car_Brand', 'Car_Model_upper', 'Car_Type'])[['Car_Door_Num']].median().reset_index()\n",
    "\n",
    "Car_median_fuel = Car_data_target[(Car_data_target['Car_Fuel(cc)'] != 0)].groupby(['Car_Brand', 'Car_Model_upper', 'Car_Type'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "  \n",
    "# Convert to integer format\n",
    "    \n",
    "Car_median_seat['Car_Seat'] = Car_median_seat['Car_Seat'].astype(int)\n",
    "\n",
    "Car_median_door['Car_Door_Num'] = Car_median_door['Car_Door_Num'].astype(int)\n",
    "\n",
    "Car_median_fuel['Car_Fuel(cc)'] = Car_median_fuel['Car_Fuel(cc)'].astype(int)\n",
    " \n",
    "#,'Car_Seat', 'Car_Door_Num']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model_upper</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aston</td>\n",
       "      <td>MARTIN</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>4735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aston</td>\n",
       "      <td>MARTIN</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Audi</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>1.8T</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>1798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Brand Car_Model_upper       Car_Type  Car_Fuel(cc)\n",
       "0     Aston          MARTIN          Coupe          4735\n",
       "1     Aston          MARTIN          Sedan          6000\n",
       "2      Audi             1.8          Sedan          1800\n",
       "3      Audi            1.8T  Station Wagon          1798\n",
       "4      Audi             2.0          Coupe          1984"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_median_fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1506 entries, 0 to 1505\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Car_Brand        1506 non-null   object\n",
      " 1   Car_Model_upper  1506 non-null   object\n",
      " 2   Car_Type         1506 non-null   object\n",
      " 3   Car_Door_Num     1506 non-null   int32 \n",
      "dtypes: int32(1), object(3)\n",
      "memory usage: 41.3+ KB\n"
     ]
    }
   ],
   "source": [
    "Car_median_door.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat = pd.merge(Car_data_target, Car_median_seat, on=['Car_Brand', 'Car_Model_upper', 'Car_Type'], how = 'left')\n",
    "\n",
    "# Assign median seat number if seat number is null \n",
    "Car_data_target_seat.loc[Car_data_target_seat['Car_Seat_x'] == 0, 'Car_Seat_x'] = Car_data_target_seat.loc[Car_data_target_seat['Car_Seat_x'] == 0, 'Car_Seat_y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18       5.0\n",
       "20       7.0\n",
       "33       7.0\n",
       "37       5.0\n",
       "44       NaN\n",
       "        ... \n",
       "13386    5.0\n",
       "13390    5.0\n",
       "13392    4.0\n",
       "13416    5.0\n",
       "13434    7.0\n",
       "Name: Car_Seat_x, Length: 2645, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat.loc[Car_data_target[(Car_data_target['Car_Seat'] == 0)].index, 'Car_Seat_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18       5.0\n",
       "20       7.0\n",
       "33       7.0\n",
       "37       5.0\n",
       "44       NaN\n",
       "        ... \n",
       "13386    5.0\n",
       "13390    5.0\n",
       "13392    4.0\n",
       "13416    5.0\n",
       "13434    7.0\n",
       "Name: Car_Seat_y, Length: 2645, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat.loc[Car_data_target[(Car_data_target['Car_Seat'] == 0)].index, 'Car_Seat_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door = pd.merge(Car_data_target_seat, Car_median_door, on=['Car_Brand', 'Car_Model_upper', 'Car_Type'], how = 'left')\n",
    "\n",
    "# Assign median door number if seat number is null \n",
    "Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_x'] = Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13439, 19)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5.0\n",
       "1        5.0\n",
       "20       5.0\n",
       "33       5.0\n",
       "35       4.0\n",
       "        ... \n",
       "13400    2.0\n",
       "13406    4.0\n",
       "13413    5.0\n",
       "13416    5.0\n",
       "13434    5.0\n",
       "Name: Car_Door_Num_x, Length: 2531, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5.0\n",
       "1        5.0\n",
       "20       5.0\n",
       "33       5.0\n",
       "35       4.0\n",
       "        ... \n",
       "13400    2.0\n",
       "13406    4.0\n",
       "13413    5.0\n",
       "13416    5.0\n",
       "13434    5.0\n",
       "Name: Car_Door_Num_y, Length: 2531, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door.loc[Car_data_target[(Car_data_target['Car_Door_Num'] == 0)].index, 'Car_Door_Num_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door_fuel = pd.merge(Car_data_target_seat_door, Car_median_fuel, on=['Car_Brand', 'Car_Model_upper', 'Car_Type'], how = 'left')\n",
    "\n",
    "# Assign median door number if seat number is null \n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_x'] = Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44          NaN\n",
       "127         NaN\n",
       "138      2350.0\n",
       "167      1240.0\n",
       "218      2400.0\n",
       "          ...  \n",
       "13371    1500.0\n",
       "13372    1800.0\n",
       "13374    1500.0\n",
       "13390    3498.0\n",
       "13396    2000.0\n",
       "Name: Car_Fuel(cc)_x, Length: 725, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44          NaN\n",
       "127         NaN\n",
       "138      2350.0\n",
       "167      1240.0\n",
       "218      2400.0\n",
       "          ...  \n",
       "13371    1500.0\n",
       "13372    1800.0\n",
       "13374    1500.0\n",
       "13390    3498.0\n",
       "13396    2000.0\n",
       "Name: Car_Fuel(cc)_y, Length: 725, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.loc[Car_data_target[(Car_data_target['Car_Fuel(cc)'] == 0)].index, 'Car_Fuel(cc)_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_columns = ['Car_No', 'Car_Price', 'Car_ORC', 'Car_Title', 'Car_Year', 'Car_Brand',\n",
    "       'Car_Model', 'Car_Location', 'Car_Mileage(km)', 'Car_Type', 'Car_Gear',\n",
    "       'Car_Seat', 'Car_Fuel(cc)', 'Car_Door_Num', 'Car_Fuel_Type',\n",
    "       'Car_Color']\n",
    "Car_data_target_seat_door_fuel.drop(['Car_Seat_y', 'Car_Door_Num_y', 'Car_Model_upper', 'Car_Fuel(cc)_y'], axis=1, inplace=True) \n",
    "Car_data_target_seat_door_fuel.columns = data_columns \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86261</td>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005 Nissan Tiida Axis</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Tiida</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79395</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009 Toyota Wish</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Wish</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85473</td>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88342</td>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72923</td>\n",
       "      <td>9900</td>\n",
       "      <td>1</td>\n",
       "      <td>2012 Nissan Serena 20X</td>\n",
       "      <td>2012</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Serena</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>107126</td>\n",
       "      <td>Van</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Light Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_No Car_Price Car_ORC                                          Car_Title  \\\n",
       "0  86261      3997       0                             2005 Nissan Tiida Axis   \n",
       "1  79395      7997       0                                   2009 Toyota Wish   \n",
       "2  85473     9940        1  2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...   \n",
       "3  88342     8940        1  2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...   \n",
       "4  72923     9900        1                             2012 Nissan Serena 20X   \n",
       "\n",
       "  Car_Year   Car_Brand  Car_Model Car_Location Car_Mileage(km)       Car_Type  \\\n",
       "0     2005      Nissan      Tiida      Dunedin          138150      Hatchback   \n",
       "1     2009      Toyota       Wish      Dunedin          100300  Station Wagon   \n",
       "2     2007  Mitsubishi  OUTLANDER     Auckland          102425         RV/SUV   \n",
       "3     2006  Mitsubishi  OUTLANDER     Auckland          109425         RV/SUV   \n",
       "4     2012      Nissan     Serena     Auckland          107126            Van   \n",
       "\n",
       "    Car_Gear  Car_Seat  Car_Fuel(cc)  Car_Door_Num Car_Fuel_Type   Car_Color  \n",
       "0  Automatic       5.0        1500.0           5.0        Petrol      Silver  \n",
       "1  Automatic       7.0        1800.0           5.0        Petrol       White  \n",
       "2  Automatic       5.0        2400.0           5.0        Petrol      Silver  \n",
       "3  Automatic       5.0        2400.0           5.0        Petrol       Black  \n",
       "4  Automatic       8.0        2000.0           5.0           NaN  Light Blue  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>1990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2455</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2708</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3804</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4265</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4448</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4562</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Outlander</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>2350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9542</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11500</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>2356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12776</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Car_Brand  Car_Model       Car_Type  Car_Fuel(cc)\n",
       "2      Mitsubishi  OUTLANDER         RV/SUV        2400.0\n",
       "64     Mitsubishi  Outlander         RV/SUV        2400.0\n",
       "85     Mitsubishi  Outlander         RV/SUV        2359.0\n",
       "175    Mitsubishi  Outlander  Station Wagon        2359.0\n",
       "273    Mitsubishi  Outlander  Station Wagon        2997.0\n",
       "331    Mitsubishi  Outlander         RV/SUV        2352.0\n",
       "592    Mitsubishi  Outlander         RV/SUV        1990.0\n",
       "723    Mitsubishi  OUTLANDER  Station Wagon        2400.0\n",
       "1049   Mitsubishi  Outlander         RV/SUV           0.0\n",
       "1147   Mitsubishi  Outlander         RV/SUV        2000.0\n",
       "1671   Mitsubishi  Outlander  Station Wagon        1998.0\n",
       "2156   Mitsubishi  Outlander         RV/SUV        2300.0\n",
       "2455   Mitsubishi  Outlander  Station Wagon           0.0\n",
       "2708   Mitsubishi  Outlander         RV/SUV        2350.0\n",
       "3804   Mitsubishi  Outlander         RV/SUV        2360.0\n",
       "4265   Mitsubishi  Outlander  Station Wagon        2400.0\n",
       "4448   Mitsubishi  Outlander  Station Wagon        2379.0\n",
       "4562   Mitsubishi  Outlander  Station Wagon        2350.0\n",
       "9542   Mitsubishi  OUTLANDER         RV/SUV        2350.0\n",
       "11500  Mitsubishi  OUTLANDER         RV/SUV        2356.0\n",
       "12776  Mitsubishi  OUTLANDER         RV/SUV           0.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Car_data_target_seat_door.groupby(['Car_Brand', 'Car_Model', 'Car_Type'])[['Car_Fuel(cc)']]\n",
    "\n",
    "Car_data_target_seat_door_fuel[['Car_Brand', 'Car_Model', 'Car_Type', 'Car_Fuel(cc)']][(Car_data_target_seat_door['Car_Model'].str.upper() == 'OUTLANDER')].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13439 entries, 0 to 13438\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Car_No           13439 non-null  object \n",
      " 1   Car_Price        12576 non-null  object \n",
      " 2   Car_ORC          13439 non-null  object \n",
      " 3   Car_Title        13439 non-null  object \n",
      " 4   Car_Year         13439 non-null  object \n",
      " 5   Car_Brand        13439 non-null  object \n",
      " 6   Car_Model        13348 non-null  object \n",
      " 7   Car_Location     13439 non-null  object \n",
      " 8   Car_Mileage(km)  13439 non-null  object \n",
      " 9   Car_Type         13438 non-null  object \n",
      " 10  Car_Gear         13406 non-null  object \n",
      " 11  Car_Seat         13159 non-null  float64\n",
      " 12  Car_Fuel(cc)     13410 non-null  float64\n",
      " 13  Car_Door_Num     13351 non-null  float64\n",
      " 14  Car_Fuel_Type    12943 non-null  object \n",
      " 15  Car_Color        11831 non-null  object \n",
      "dtypes: float64(3), object(13)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Color          0.119652\n",
       "Car_Price          0.064216\n",
       "Car_Fuel_Type      0.036908\n",
       "Car_Seat           0.020835\n",
       "Car_Model          0.006771\n",
       "Car_Door_Num       0.006548\n",
       "Car_Gear           0.002456\n",
       "Car_Fuel(cc)       0.002158\n",
       "Car_Type           0.000074\n",
       "Car_Mileage(km)    0.000000\n",
       "Car_Location       0.000000\n",
       "Car_Brand          0.000000\n",
       "Car_Year           0.000000\n",
       "Car_Title          0.000000\n",
       "Car_ORC            0.000000\n",
       "Car_No             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target_seat_door_fuel.isnull().sum()/len(Car_data_target_seat_door))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop rows with null price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door_fuel.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Color          0.0\n",
       "Car_Fuel_Type      0.0\n",
       "Car_Door_Num       0.0\n",
       "Car_Fuel(cc)       0.0\n",
       "Car_Seat           0.0\n",
       "Car_Gear           0.0\n",
       "Car_Type           0.0\n",
       "Car_Mileage(km)    0.0\n",
       "Car_Location       0.0\n",
       "Car_Model          0.0\n",
       "Car_Brand          0.0\n",
       "Car_Year           0.0\n",
       "Car_Title          0.0\n",
       "Car_ORC            0.0\n",
       "Car_Price          0.0\n",
       "Car_No             0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target_seat_door_fuel.isnull().sum()/len(Car_data_target_seat_door))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10360, 16)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete records with P.O.A price\n",
    "Car_data_target_seat_door_fuel.drop(Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Price'] == 'P.0.A')].index, axis=0, inplace = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Color          0.0\n",
       "Car_Fuel_Type      0.0\n",
       "Car_Door_Num       0.0\n",
       "Car_Fuel(cc)       0.0\n",
       "Car_Seat           0.0\n",
       "Car_Gear           0.0\n",
       "Car_Type           0.0\n",
       "Car_Mileage(km)    0.0\n",
       "Car_Location       0.0\n",
       "Car_Model          0.0\n",
       "Car_Brand          0.0\n",
       "Car_Year           0.0\n",
       "Car_Title          0.0\n",
       "Car_ORC            0.0\n",
       "Car_Price          0.0\n",
       "Car_No             0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = (Car_data_target_seat_door_fuel.isnull().sum()/len(Car_data_target_seat_door))\n",
    "missing.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to integer for numeric columns \n",
    "Car_data_target_seat_door_fuel['Car_Price'] = Car_data_target_seat_door_fuel['Car_Price'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Year'] = Car_data_target_seat_door_fuel['Car_Year'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Mileage(km)'] = Car_data_target_seat_door_fuel['Car_Mileage(km)'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Fuel(cc)'] = Car_data_target_seat_door_fuel['Car_Fuel(cc)'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_Price'] = Car_data_target_seat_door_fuel['Car_Price'].astype('int')\n",
    "Car_data_target_seat_door_fuel['Car_ORC'] = Car_data_target_seat_door_fuel['Car_ORC'].astype('int')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10069, 16)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10069 entries, 0 to 13438\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Car_No           10069 non-null  object \n",
      " 1   Car_Price        10069 non-null  int32  \n",
      " 2   Car_ORC          10069 non-null  int32  \n",
      " 3   Car_Title        10069 non-null  object \n",
      " 4   Car_Year         10069 non-null  int32  \n",
      " 5   Car_Brand        10069 non-null  object \n",
      " 6   Car_Model        10069 non-null  object \n",
      " 7   Car_Location     10069 non-null  object \n",
      " 8   Car_Mileage(km)  10069 non-null  int32  \n",
      " 9   Car_Type         10069 non-null  object \n",
      " 10  Car_Gear         10069 non-null  object \n",
      " 11  Car_Seat         10069 non-null  float64\n",
      " 12  Car_Fuel(cc)     10069 non-null  int32  \n",
      " 13  Car_Door_Num     10069 non-null  float64\n",
      " 14  Car_Fuel_Type    10069 non-null  object \n",
      " 15  Car_Color        10069 non-null  object \n",
      "dtypes: float64(2), int32(5), object(9)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\env\\ad\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Car Fuel(cc)')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Car_data_target_seat_door_fuel['Car_Fuel(cc)'])\n",
    "plt.title('Distribution of Car Fuel(cc)', fontsize = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete Outlier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>76494</td>\n",
       "      <td>6950</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Honda Odyssey</td>\n",
       "      <td>2006</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Odyssey</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>111889</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>111889</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4006</th>\n",
       "      <td>76004</td>\n",
       "      <td>17495</td>\n",
       "      <td>1</td>\n",
       "      <td>2011 Audi Cabriolet</td>\n",
       "      <td>2011</td>\n",
       "      <td>Audi</td>\n",
       "      <td>Cabriolet</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>57325</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>Tiptronic</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Grey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10963</th>\n",
       "      <td>31643</td>\n",
       "      <td>9440</td>\n",
       "      <td>1</td>\n",
       "      <td>2008 Toyota Auris</td>\n",
       "      <td>2008</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Auris</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>98000</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>98000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12884</th>\n",
       "      <td>23440</td>\n",
       "      <td>12990</td>\n",
       "      <td>1</td>\n",
       "      <td>2008 Toyota Harrier</td>\n",
       "      <td>2008</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Harrier</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>97500</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23600</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Car_No  Car_Price  Car_ORC            Car_Title  Car_Year Car_Brand  \\\n",
       "3999   76494       6950        1   2006 Honda Odyssey      2006     Honda   \n",
       "4006   76004      17495        1  2011 Audi Cabriolet      2011      Audi   \n",
       "10963  31643       9440        1    2008 Toyota Auris      2008    Toyota   \n",
       "12884  23440      12990        1  2008 Toyota Harrier      2008    Toyota   \n",
       "\n",
       "       Car_Model Car_Location  Car_Mileage(km)       Car_Type   Car_Gear  \\\n",
       "3999     Odyssey     Auckland           111889  Station Wagon  Automatic   \n",
       "4006   Cabriolet     Auckland            57325    Convertible  Tiptronic   \n",
       "10963      Auris     Auckland            98000      Hatchback  Automatic   \n",
       "12884    Harrier     Auckland            97500         RV/SUV  Automatic   \n",
       "\n",
       "       Car_Seat  Car_Fuel(cc)  Car_Door_Num Car_Fuel_Type Car_Color  \n",
       "3999        7.0        111889           5.0        Petrol     White  \n",
       "4006        4.0         20000           2.0        Petrol      Grey  \n",
       "10963       5.0         98000           5.0        Petrol       Red  \n",
       "12884       5.0         23600           4.0        Petrol     Black  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Fuel(cc)'] > 10000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete 76004\n",
    "\n",
    "Car_data_target_seat_door_fuel.drop(Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '76004')].index, axis=0, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_Odyssey = Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Model'] == 'Odyssey') & (Car_data_target_seat_door_fuel['Car_Type'] == 'Station Wagon') & (Car_data_target_seat_door_fuel['Car_Brand'] == 'Honda')].groupby(['Car_Model', 'Car_Type', 'Car_Brand'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "\n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '76494')].index, 'Car_Fuel(cc)'] = median_Odyssey.loc[0, 'Car_Fuel(cc)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_Auris = Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Model'] == 'Auris') & (Car_data_target_seat_door_fuel['Car_Type'] == 'Hatchback') & (Car_data_target_seat_door_fuel['Car_Brand'] == 'Toyota')].groupby(['Car_Model', 'Car_Type'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '31643')].index, 'Car_Fuel(cc)'] = median_Auris.loc[0, 'Car_Fuel(cc)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_Harrier = Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Model'] == 'Harrier') & (Car_data_target_seat_door_fuel['Car_Type'] == 'RV/SUV') & (Car_data_target_seat_door_fuel['Car_Brand'] == 'Toyota')].groupby(['Car_Model', 'Car_Type', 'Car_Brand'])[['Car_Fuel(cc)']].median().reset_index()\n",
    "Car_data_target_seat_door_fuel.loc[Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_No'] == '23440')].index, 'Car_Fuel(cc)'] = median_Harrier.loc[0, 'Car_Fuel(cc)']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\env\\ad\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Car Fuel(cc)')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Car_data_target_seat_door_fuel['Car_Fuel(cc)'])\n",
    "plt.title('Distribution of Car Fuel(cc)', fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\env\\ad\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Car Mileage(km)')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Car_data_target_seat_door_fuel['Car_Mileage(km)'])\n",
    "plt.title('Distribution of Car Mileage(km)', fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.plot(kind=\"scatter\", x=\"Car_Seat\", y=\"Car_Price\")\n",
    "plt.title('Car Price for different Number of Seats', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Seat', fontsize = 15) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_No</th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Title</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Model</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "      <th>Car_Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86261</td>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005 Nissan Tiida Axis</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Tiida</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79395</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009 Toyota Wish</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Wish</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1800</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85473</td>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88342</td>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>OUTLANDER</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>71719</td>\n",
       "      <td>9400</td>\n",
       "      <td>1</td>\n",
       "      <td>2010 Nissan X-Trail 20XT</td>\n",
       "      <td>2010</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>X-Trail</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>125384</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_No  Car_Price  Car_ORC  \\\n",
       "0  86261       3997        0   \n",
       "1  79395       7997        0   \n",
       "2  85473       9940        1   \n",
       "3  88342       8940        1   \n",
       "5  71719       9400        1   \n",
       "\n",
       "                                           Car_Title  Car_Year   Car_Brand  \\\n",
       "0                             2005 Nissan Tiida Axis      2005      Nissan   \n",
       "1                                   2009 Toyota Wish      2009      Toyota   \n",
       "2  2007 Mitsubishi OUTLANDER 7 SEATER 2WD/4WD **L...      2007  Mitsubishi   \n",
       "3  2006 Mitsubishi OUTLANDER 4WD/2WD **LOW KMS** ...      2006  Mitsubishi   \n",
       "5                           2010 Nissan X-Trail 20XT      2010      Nissan   \n",
       "\n",
       "   Car_Model Car_Location  Car_Mileage(km)       Car_Type   Car_Gear  \\\n",
       "0      Tiida      Dunedin           138150      Hatchback  Automatic   \n",
       "1       Wish      Dunedin           100300  Station Wagon  Automatic   \n",
       "2  OUTLANDER     Auckland           102425         RV/SUV  Automatic   \n",
       "3  OUTLANDER     Auckland           109425         RV/SUV  Automatic   \n",
       "5    X-Trail     Auckland           125384         RV/SUV  Automatic   \n",
       "\n",
       "   Car_Seat  Car_Fuel(cc)  Car_Door_Num Car_Fuel_Type Car_Color  \n",
       "0       5.0          1500           5.0        Petrol    Silver  \n",
       "1       7.0          1800           5.0        Petrol     White  \n",
       "2       5.0          2400           5.0        Petrol    Silver  \n",
       "3       5.0          2400           5.0        Petrol     Black  \n",
       "5       5.0          2000           5.0        Petrol     Black  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.plot(kind=\"scatter\", x=\"Car_Year\", y=\"Car_Price\")\n",
    "plt.title('Car Price for different year of car', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Year', fontsize = 15) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete outlier: Car year earlier than 1985. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "Car_data_target_seat_door_fuel.drop(Car_data_target_seat_door_fuel[(Car_data_target_seat_door_fuel['Car_Year'] < 1990)].index, axis=0, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.plot(kind=\"scatter\", x=\"Car_Year\", y=\"Car_Price\")\n",
    "plt.title('Car Price for different year of car', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Year', fontsize = 15) \n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#box plot Districts/Total Price\n",
    "f, ax = plt.subplots(figsize=(15, 10))\n",
    "fig = sns.boxplot(x='Car_Type', y=\"Car_Price\", data=Car_data_target_seat_door_fuel)\n",
    "plt.title('Car Price for Different Car Type', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Type', fontsize = 15) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x=\"Car_Brand\", y=\"Car_Price\", \n",
    "              data=Car_data_target_seat_door_fuel, jitter=True,\n",
    "              palette=\"Set2\",  # 设置调色盘\n",
    "              dodge=True,  # 是否拆分\n",
    "             )\n",
    "plt.title('Car Price for Different Car Brand', fontsize = 20) \n",
    "plt.ylabel('Car Price', fontsize = 15) \n",
    "plt.xlabel('Car Brand', fontsize = 15) \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10065 entries, 0 to 13438\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Car_No           10065 non-null  object \n",
      " 1   Car_Price        10065 non-null  int32  \n",
      " 2   Car_ORC          10065 non-null  int32  \n",
      " 3   Car_Title        10065 non-null  object \n",
      " 4   Car_Year         10065 non-null  int32  \n",
      " 5   Car_Brand        10065 non-null  object \n",
      " 6   Car_Model        10065 non-null  object \n",
      " 7   Car_Location     10065 non-null  object \n",
      " 8   Car_Mileage(km)  10065 non-null  int32  \n",
      " 9   Car_Type         10065 non-null  object \n",
      " 10  Car_Gear         10065 non-null  object \n",
      " 11  Car_Seat         10065 non-null  float64\n",
      " 12  Car_Fuel(cc)     10065 non-null  int32  \n",
      " 13  Car_Door_Num     10065 non-null  float64\n",
      " 14  Car_Fuel_Type    10065 non-null  object \n",
      " 15  Car_Color        10065 non-null  object \n",
      "dtypes: float64(2), int32(5), object(9)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "Car_data_target_seat_door_fuel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only keep valuable columns \n",
    "\n",
    "Car_data_target_final = Car_data_target_seat_door_fuel.drop(['Car_No', 'Car_Title', 'Car_Model', 'Car_Color'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Car_ORC</th>\n",
       "      <th>Car_Year</th>\n",
       "      <th>Car_Brand</th>\n",
       "      <th>Car_Location</th>\n",
       "      <th>Car_Mileage(km)</th>\n",
       "      <th>Car_Type</th>\n",
       "      <th>Car_Gear</th>\n",
       "      <th>Car_Seat</th>\n",
       "      <th>Car_Fuel(cc)</th>\n",
       "      <th>Car_Door_Num</th>\n",
       "      <th>Car_Fuel_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3997</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>138150</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>Dunedin</td>\n",
       "      <td>100300</td>\n",
       "      <td>Station Wagon</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1800</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9940</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>102425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8940</td>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>109425</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9400</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>Auckland</td>\n",
       "      <td>125384</td>\n",
       "      <td>RV/SUV</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Petrol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Car_Price  Car_ORC  Car_Year   Car_Brand Car_Location  Car_Mileage(km)  \\\n",
       "0       3997        0      2005      Nissan      Dunedin           138150   \n",
       "1       7997        0      2009      Toyota      Dunedin           100300   \n",
       "2       9940        1      2007  Mitsubishi     Auckland           102425   \n",
       "3       8940        1      2006  Mitsubishi     Auckland           109425   \n",
       "5       9400        1      2010      Nissan     Auckland           125384   \n",
       "\n",
       "        Car_Type   Car_Gear  Car_Seat  Car_Fuel(cc)  Car_Door_Num  \\\n",
       "0      Hatchback  Automatic       5.0          1500           5.0   \n",
       "1  Station Wagon  Automatic       7.0          1800           5.0   \n",
       "2         RV/SUV  Automatic       5.0          2400           5.0   \n",
       "3         RV/SUV  Automatic       5.0          2400           5.0   \n",
       "5         RV/SUV  Automatic       5.0          2000           5.0   \n",
       "\n",
       "  Car_Fuel_Type  \n",
       "0        Petrol  \n",
       "1        Petrol  \n",
       "2        Petrol  \n",
       "3        Petrol  \n",
       "5        Petrol  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car_data_target_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical features are:  ['Car_Brand', 'Car_Location', 'Car_Type', 'Car_Gear', 'Car_Fuel_Type']\n",
      "Numerical features are:  ['Car_Price', 'Car_ORC', 'Car_Year', 'Car_Mileage(km)', 'Car_Seat', 'Car_Fuel(cc)', 'Car_Door_Num']\n"
     ]
    }
   ],
   "source": [
    "# distinguish features into categorical and numerical features \n",
    "\n",
    "categorical_features = Car_data_target_final.select_dtypes(include = [\"object\"]).columns\n",
    "numerical_features = Car_data_target_final.select_dtypes(exclude = [\"object\"]).columns\n",
    "print('Categorical features are: ', categorical_features.tolist())\n",
    "print('Numerical features are: ',numerical_features.tolist())\n",
    "\n",
    "Car_data_categorical_columns = Car_data_target_final[categorical_features]\n",
    "\n",
    "Car_data_numerical_columns = Car_data_target_final[numerical_features].drop(['Car_Price'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dummy values \n",
    "\n",
    "df_categorical_columns_dummies = pd.get_dummies(Car_data_categorical_columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Brand_Aston</th>\n",
       "      <th>Car_Brand_Audi</th>\n",
       "      <th>Car_Brand_BMW</th>\n",
       "      <th>Car_Brand_Bentley</th>\n",
       "      <th>Car_Brand_Cadillac</th>\n",
       "      <th>Car_Brand_Chevrolet</th>\n",
       "      <th>Car_Brand_Chrysler</th>\n",
       "      <th>Car_Brand_Citroen</th>\n",
       "      <th>Car_Brand_Daihatsu</th>\n",
       "      <th>Car_Brand_Fiat</th>\n",
       "      <th>...</th>\n",
       "      <th>Car_Type_Ute</th>\n",
       "      <th>Car_Type_Van</th>\n",
       "      <th>Car_Gear_Automatic</th>\n",
       "      <th>Car_Gear_Manual</th>\n",
       "      <th>Car_Gear_Tiptronic</th>\n",
       "      <th>Car_Fuel_Type_Diesel</th>\n",
       "      <th>Car_Fuel_Type_Electric</th>\n",
       "      <th>Car_Fuel_Type_Hybrid</th>\n",
       "      <th>Car_Fuel_Type_LPG</th>\n",
       "      <th>Car_Fuel_Type_Petrol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Car_Brand_Aston  Car_Brand_Audi  Car_Brand_BMW  Car_Brand_Bentley  \\\n",
       "0                0               0              0                  0   \n",
       "1                0               0              0                  0   \n",
       "2                0               0              0                  0   \n",
       "3                0               0              0                  0   \n",
       "5                0               0              0                  0   \n",
       "\n",
       "   Car_Brand_Cadillac  Car_Brand_Chevrolet  Car_Brand_Chrysler  \\\n",
       "0                   0                    0                   0   \n",
       "1                   0                    0                   0   \n",
       "2                   0                    0                   0   \n",
       "3                   0                    0                   0   \n",
       "5                   0                    0                   0   \n",
       "\n",
       "   Car_Brand_Citroen  Car_Brand_Daihatsu  Car_Brand_Fiat  ...  Car_Type_Ute  \\\n",
       "0                  0                   0               0  ...             0   \n",
       "1                  0                   0               0  ...             0   \n",
       "2                  0                   0               0  ...             0   \n",
       "3                  0                   0               0  ...             0   \n",
       "5                  0                   0               0  ...             0   \n",
       "\n",
       "   Car_Type_Van  Car_Gear_Automatic  Car_Gear_Manual  Car_Gear_Tiptronic  \\\n",
       "0             0                   1                0                   0   \n",
       "1             0                   1                0                   0   \n",
       "2             0                   1                0                   0   \n",
       "3             0                   1                0                   0   \n",
       "5             0                   1                0                   0   \n",
       "\n",
       "   Car_Fuel_Type_Diesel  Car_Fuel_Type_Electric  Car_Fuel_Type_Hybrid  \\\n",
       "0                     0                       0                     0   \n",
       "1                     0                       0                     0   \n",
       "2                     0                       0                     0   \n",
       "3                     0                       0                     0   \n",
       "5                     0                       0                     0   \n",
       "\n",
       "   Car_Fuel_Type_LPG  Car_Fuel_Type_Petrol  \n",
       "0                  0                     1  \n",
       "1                  0                     1  \n",
       "2                  0                     1  \n",
       "3                  0                     1  \n",
       "5                  0                     1  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_categorical_columns_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train : (7045, 70)\n",
      "X_test : (3020, 70)\n",
      "y_train : (7045,)\n",
      "y_test : (3020,)\n"
     ]
    }
   ],
   "source": [
    "# Create dataset for ML \n",
    "# Split dataset for the training and testing \n",
    "car_train_len = int(len(Car_data_target_final)*0.7) \n",
    "\n",
    "y_train = Car_data_target_final['Car_Price'][:car_train_len]\n",
    "\n",
    "y_test = Car_data_target_final['Car_Price'][car_train_len:]\n",
    "\n",
    "\n",
    "df_train_num = Car_data_numerical_columns[:car_train_len] \n",
    "df_train_dum = df_categorical_columns_dummies[:car_train_len]\n",
    "X_train = pd.concat([df_train_num, df_train_dum], axis = 1)\n",
    "df_train = pd.concat([X_train, y_train], axis = 1)\n",
    "\n",
    "\n",
    "df_test_num = Car_data_numerical_columns[car_train_len:] \n",
    "df_test_dum = df_categorical_columns_dummies[car_train_len:]\n",
    "X_test = pd.concat([df_test_num, df_test_dum], axis = 1)\n",
    "df_test = pd.concat([X_test, y_test], axis = 1)\n",
    "\n",
    "\n",
    "print(\"X_train : \" + str(X_train.shape))\n",
    "print(\"X_test : \" + str(X_test.shape))\n",
    "print(\"y_train : \" + str(y_train.shape))\n",
    "print(\"y_test : \" + str(y_test.shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Price                1.000000\n",
       "Car_Fuel(cc)             0.417299\n",
       "Car_Year                 0.381062\n",
       "Car_Fuel_Type_Diesel     0.367488\n",
       "Car_Brand_Lamborghini    0.363655\n",
       "                           ...   \n",
       "Car_Brand_Smart          0.001320\n",
       "Car_Brand_Cadillac       0.001038\n",
       "Car_Type_Sedan           0.001023\n",
       "Car_Brand_Renault             NaN\n",
       "Car_Brand_Rolls-Royce         NaN\n",
       "Name: Car_Price, Length: 71, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train_corr = pd.concat([X_train, y_train], axis = 1)\n",
    "corr_value = df_train_corr.corr().abs()\n",
    "sns.heatmap(corr_value,square=True,vmax=1,vmin=-1,center=0.0,cmap='coolwarm')\n",
    "corr_value.sort_values([\"Car_Price\"], ascending = False, inplace = True)\n",
    "corr_value[\"Car_Price\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature selection by using correlated features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use top 15 correlated features to train four regression models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = corr_value[\"Car_Price\"][1:16].index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reconstruct DataFrame for training and testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_train_x = pd.DataFrame(X_train,columns = features)\n",
    "selected_test_x = pd.DataFrame(X_test,columns = features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Both training data and training label are converted to a normal distribution with a mean of 0 and a variance of 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# data normalization both label and training data\n",
    "ss_x = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "ss_train_x = ss_x.fit_transform(selected_train_x)\n",
    "ss_test_x = ss_x.transform(selected_test_x)\n",
    "ss_train_y = ss_y.fit_transform(np.array(y_train).reshape(-1,1))\n",
    "ss_test_y = ss_y.transform(np.array(y_test).reshape(-1,1))\n",
    "mses = []\n",
    "r2_scores = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Four regression models\n",
    "### Linear model\n",
    "#### R2 score and mean squared score  are used to measure the performance of the linear regression model.\n",
    "#### Select out first first 100 test labels and predict labels for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 170452155.29873404\n",
      "r2_score: 0.39502073764802337\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear Regression\n",
    "lr = LinearRegression().fit(ss_train_x,ss_train_y)\n",
    "ss_pred = lr.predict(ss_test_x)\n",
    "ss_lr_pred = ss_y.inverse_transform(ss_pred)\n",
    "print(\"mse:\",mean_squared_error(ss_lr_pred, y_test))\n",
    "print(\"r2_score:\",r2_score(ss_lr_pred,y_test))\n",
    "plt.plot(list(range(len(ss_lr_pred[:100]))),ss_lr_pred[:100])\n",
    "plt.plot(list(range(len(y_test[:100]))),y_test[:100])\n",
    "plt.legend(['pred','true'])\n",
    "plt.show()\n",
    "plt.savefig(\"lr.png\")\n",
    "mses.append(mean_squared_error(ss_lr_pred, y_test))\n",
    "r2_scores.append(r2_score(ss_lr_pred,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM model\n",
    "#### R2 score and mean squared score  are used to measure the performance of the svm regression model.\n",
    "#### Select out the first 100 test labels and predict labels for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 110284392.18729502\n",
      "r2_score: 0.5344224906204009\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Svm Regression\n",
    "from sklearn.svm import SVR\n",
    "svr = SVR(kernel='rbf',C=10,gamma=0.1,coef0=0.1)\n",
    "svr.fit(ss_train_x, ss_train_y.ravel())\n",
    "svr_pred = svr.predict(ss_test_x)\n",
    "ss_svr_pred = ss_y.inverse_transform(svr_pred)\n",
    "print(\"mse:\",mean_squared_error(ss_svr_pred,y_test))\n",
    "print(\"r2_score:\",r2_score(ss_svr_pred,y_test))\n",
    "plt.plot(list(range(len(ss_svr_pred[:100]))),ss_svr_pred[:100])\n",
    "plt.plot(list(range(len(y_test[:100]))),y_test[:100])\n",
    "plt.legend(['pred','true'])\n",
    "plt.show()\n",
    "plt.savefig(\"svr.png\")\n",
    "mses.append(mean_squared_error(ss_svr_pred,y_test))\n",
    "r2_scores.append(r2_score(ss_svr_pred,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso model\n",
    "#### R2 score and mean squared score  are used to measure the performance of the lasso model.\n",
    "#### Select out the first 100 test labels and predict labels for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 165848038.48942965\n",
      "r2_score: 0.3280742050505936\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lasso Regression\n",
    "from sklearn.linear_model import Lasso\n",
    "la = Lasso(alpha=0.02,fit_intercept=True)\n",
    "la.fit(ss_train_x,ss_train_y)\n",
    "la_pred = la.predict(ss_test_x)\n",
    "ss_la_pred = ss_y.inverse_transform(la_pred)\n",
    "print(\"mse:\",mean_squared_error(ss_la_pred,y_test))\n",
    "print(\"r2_score:\",r2_score(ss_la_pred,y_test))\n",
    "plt.plot(list(range(len(ss_la_pred[:100]))),ss_la_pred[:100])\n",
    "plt.plot(list(range(len(y_test[:100]))),y_test[:100])\n",
    "plt.legend(['pred','true'])\n",
    "plt.show()\n",
    "plt.savefig(\"lg.png\")\n",
    "mses.append(mean_squared_error(ss_la_pred,y_test))\n",
    "r2_scores.append(r2_score(ss_la_pred,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elastic net model\n",
    "#### R2 score and mean squared score  are used to measure the performance of the elastic net regression model.\n",
    "#### Select out the first 100 test labels and predict labels for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 177703849.62943965\n",
      "r2_score: -0.5044816907722205\n"
     ]
    },
    {
     "data": {
      "image/png": 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kNb5tbD8ghLg0ZPtKY9thIcS9I3h+Gs3Y0ydmoN1EmujjRCyDe4B9IY9/AfxOSjkZaAE+bWz/NNBibP+dsR9CiJnAjcAsYCXwZ0NgzMD9wGXATOAmY1+N5vSgd8xAF51popAhiYEQogC4AnjIeCyAFcCzxi6PAtcY9682HmM8f6Gx/9XAU1JKt5TyGHAYWGz8HZZSHpVSdgNPGftqNKcHvWMG2jIYnKYj8Mjl0Nk81iMZVwzVMrgP+CYQWLQ1HXBIKQPf5kog37ifD1QAGM87jf2D23sdM9D2Pggh7hJCbBZCbG5oaBji0DWaMaZ3zMCsK5AHZf9/oew9qN871iMZVxxXDIQQVwL1UsotERjPoEgpH5RSLpJSLsrMzBzr4Wg0Q2OgCmTtJuqf6m3qVlsGEcUyhH2WAVcJIS4HbEAy8HvALoSwGLP/AqDK2L8KKAQqhRAWIAVoCtkeIPSYgbZrNKc+vSuQdW+iwaneqm47m8Z2HOOM41oGUspvSykLpJTFqADwW1LKW4C3geuM3W4HXjDurzYeYzz/lpRSGttvNLKNSoApwIfAJmCKkZ0UY7zH6hE5O40mGuidTaTbUQxMZzO0lKr7XdoyiCTDqTP4FvA/QojDqJjAKmP7KiDd2P4/wL0AUso9wL+AvcCrwBeklD7Dsvgi8BoqW+lfxr4azemBcdH3YOYzj25mb22n2q4tg74EXESg3UQRZihuoiBSyrXAWuP+UVQmUO99XMD1Axz/U+Cn/Wx/GXj5RMai0ZwyGBf9+k7Jmn11LJ0Qz0zQRWf9YbiI2kUC1tYGYsd4OOMJXYGs0Yw2hmXgdEsAWoxb3Y6iH6q20ZZQTJkvg05H/ViPZlyhxUCjGW0MC6DFpUTA2WVkaGs3UV+qt1GXNJNmmXT8mEFLmRbUEUSLgUYz2hgXLEdADFxeFUzWbqJw2mqhrZrS2Gk4SMTscgy8r8sJfzoTdj8XseGd7mgx0GhGG+Oi3xS0DDyq8ExnE4VTpeIFB02TaZZJxHS3DLxvewP43NBWHaHBnf5oMdBoRhvjot8cKgYmq3YT9aZ6Kwgzu/3FOEgi1ts2sBvI5VS37vbIje80R4uBRjPaBCwDI1bQ2uUBk1mLQW+qt0HWDGq7BC0yEYGEgVxFge3dHZEa3WmPFgONZrQxZreNnUoMtJuoH6RUbqK8+TR3dKsAMgxcaxCwDLq1ZTBSaDHQaEYbXzcAjV0+AFpdHqR2E4XjKFPZQ3kLaOroxkGi2j5QRpEWgxFHi4FGM9oYbqL6dmUZeHwSqd1E4VRvB8CTPZ82lzfEMhigP1FQDLSbaKTQYqDRjDaGm6iu00+STRX9+4VFu4lCaToMQHNCMQBtIlltP66bSIvBSKHFQKMZbfweJAKXFyZmJADgw6zrDEJpKYWELJrcqplfQloWAPK4loF2E40UWgw0mtHG5wl2LC0xxMCLGfy+sRxVdOEog9RimjtUfCU/MwO3tNDd1tj//jq1dMTRYqDRjDZ+L35jQZuSDBUY9WLWbqJQWkohtYimDjcAk7KSaCEJT9sAKxpqN9GIo8VAoxltfB4VIwAmZgYsA8u4dxNJKfH5pRJFZ1WYZTApM4EWmYivffRjBu8dbuTxD8qG/TqnOloMNJrRxu/BJwKWgRKDbmke903WVu+oZvFP1+BuKgfpA3sRzR3dCKH+Tw6ZhBxKaqmUwxrHo++X8tP/7lPCNI7RYqDRjDY+jwoYA0Xp8QgBHinGfWrpgdo2mjq6aag4oDakFtPU0U1qfAxpCTE0k4h5QDFwGHckeDqHNY66Njed3T5Km8a3y0mLgUYz2vi9eDATazGRGGshMdaiLINx7iZq6VQuodZqlVZKahHN7d2kJcSQGh+DQyZhcQ/QrM7lBKuysobrKmpodQGwp7p1WK9zqqPFQKMZbXwePJhJT4hBCEFKnBW33zTuA8hN7UoMPE3HwGSB5HyaO5QYJMdZaSGJWI8T/P7wAz0u8LogJV89dred9BiklDS0q6D1nirnSb/O6YAWA41mtPF76JZm0hJjAHrEYJynlgYsA5OjDFIKwWSmqcNNekIMZpPAZU3BhB/cvS7SbmMGn5ynbodhGbR0evD4VKxAWwYajWZ08Xnp9ptJS1Ar+qbEWXH5TOPeTRTIHErorITU4uC2tAQlmt0xqWrH3lXIgeBxcoG6HYYY1BkuopQ4K3uqnchhBqNPZbQYaEaETaXNlDaO7wDcgPg9uP0m0hN6LIMuXy83UXfHuMsuaulU55/mqYHUYnx+iaPLE/w/+WzHE4PhWwb1bcpFtHxqJi2dHmqcrpN+rVMdLQaaEeFLT2zjvjUHx3oY0YnPg8tvCs54lWUgwt1ED5wN7/1ujAYYeXx+iaOzm0TRhV224rcX0dLZjZQE/08iPk3t3DujKJBJFBSDk48Z1BuWwYrpqv3F7nEcN9BioBk2Pr+kvs1FtWP8zqoGw+f14Jbm4EUuOc5Kp0/0uIk8XaoCt27v2A0ywji7PPglLEtX7SQcsXm0GG6jtETlThMJ6Wrn3v2JApZByvDdRAHL4NwpGQgxvuMGWgw0w6ap3Y1fQl2bFoP+8HrceKU5zE3k9puQATdRe726basdoxFGnkC84Ow0JQaVZNFkbAv8n6yJGWrnUXQTNbS5SYq1kJ4Yy8SMBC0GGs1wCMyuap2ucR2AGwif14OXcMvAg6UfMRg/i7sHMolmxzsAOOrJDApE4P8Un5SKV5rwtvdqVtdbDIaRWlrX6iIzWVkis/NT2FMdHW6ih9cf46F1RyP6nloMNMOm3rAI3F6/WtJRE4bP263qDBJ7sol8mHoqkDtCLINxIqaBC3+RqZ42GcehNnMfy8CeEEsLiX07l7qcqgusza7qE4bpJspOsgEwKy+ZGqcrOLax5F+bK3hqU0VE31OLgWbYNBiWAUBtq3YV9cbv9eDFEuYmCmtU116nbr2ugReAP80IXHATOyupMeVQ3uKi2ShCs8er/1NqfAwtMglvRz8xA1sKCAExicMUAxdZhmUwKy8FICqsgxqni4rmzoha2loMNMOmvrVHDOpC7msU0me4iUKKzjyYEQHLIOAmAmitGYMRjhBbH1N/QyAgBjHtFThteZQ3ddDc4SbJZiHGoi5LqfGqCln2FoMuB8TZ1f1hiIGUkvpWN1lJATFQq6uNddygw+3F2eXB7fWHTbRGGy0GmmFTH/KFrRvHedoDIX0efMJMUqzqXJpss+DDhJB+1WohYBkAtJ3CYrDpb7D54SHt2tLRTXyMCZOjHFdiIWXNnTR1dAetJ1AWQotMwtQntdSwDABiEk46tbS1y4vb6yfLcBPZ42PIt8eNSHppt9fP/zy9nSMNJ774To2zK3i/vHl4TfhOBC0GmmFT3+aiKD0eGNxN9OyWSn7z+oFIDSt68HswW1RfIjAsA2kxnvPib6ujQ6oL0imdUdTe0LcmYACaO7uZHNehXGOpxTg6PZQ1dQaDxwCpCVZaZGLfZnWhYhB78pZBINYVcBOBsg72joBlcLSxnee3VfHq7hP/PENTtCtatBhoTiHq29wUpMaRlhAzqBg8t6WSJz+MbFAsGhB+L2ZLz0UuOc6qVjoDVZ3sqGWPLFKPT9WMIimhowE6B+gy2ouWjm6mxyrhiMucCMC+mtZgyw5QMQMHicR0O8ID630sg5MVA2XRBiwDgKnZSZQ1d+L1+Qc6bEgEKpkP1w/PMqho7hpkz5FFi4Fm2DS0uclKspGVFBus6OyPMsMvPN4WETH5vVisPWJgNZswmw3LwOfB31ZHlcxQK3s5T8JN5KiAo++M0GhPkq4WFRB3O4fUVqO5o5uJVpUlZM+fCoDXL8PcRDarmTZTMmbpDV/4PkwMEk96HeT+LIM8e5xRRDk8X32tIQYn4yaqdriwi3YmJbi1m0hz6iCl+uFkJcWSk2Ib0DJweXzUtLrwS4Lr3J7OhKbYmqQXa0xM2POWwGO/lxhXAw3STp1Mpb3xJCynd34BT90ytmmpHSFrFXcd3zpo7uxmglDH5BRNCW4PBNkDdMfY1Z3QKuQ+lsFJikFrwDLoEYNcu7ISQmfnJ0PAMjhS337CGUHVji7+ZPsLvzH/iYpoEgMhRKEQ4m0hxF4hxB4hxD3G9jQhxBtCiEPGbaqxXQgh/iCEOCyE2CmEWBDyWrcb+x8SQtwesn2hEGKXccwfRMC5qol6Wru8dHv9TDLXMjWulVpn/xf6ypbO4LWq/hTIOFp3qIHHNp7curhv7a9j/o9e50cv7sXnl5ilF6s1NmwfS+BxVwtWv5uumHTqZCoex0m4iRr2qyCqe+SyYLq9fq574H3WHqg//s4QHgQfQtygpcNDjmyAhCwSExKDFkGoZQDgjTX6EwWqkD0u8LnDLYOTdBPVtbqJs5pJNAL7APn2OACqhtlapdYQk45u3wmnW9c4XUwTFRT5y6NLDAAv8DUp5UxgCfAFIcRM4F7gTSnlFOBN4zHAZcAU4+8u4AFQ4gF8HzgLWAx8PyAgxj53hhy3cvinpokEAVP78p1f4usHb+XirlfwePv26S9t7PlSBxYTiVaONLTzuce28OOX9tLtPTHfcbfXz49f2ke81czD7x3js49txoKP2NhwMQhYCtJZBUB23gRazOlYO+vC9ntmc8XgQUgpocFoEDiCweeDdW1sLmvhvzuH6LYKTY/t3T6iF26vj3a3lwxfHdgLAShMUwkIab3EwBdvtKQIZFkFqo9HJGbgIjs5ltC5Z26KsgyqHcO3DEzGyx6pP7HxNThaSfc3kuJtoqG1/YS/gyfLccVASlkjpdxq3G8D9gH5wNXAo8ZujwLXGPevBv4hFRsBuxAiF7gUeENK2SylbAHeAFYazyVLKTdKZU/9I+S1NFFOfZsbE34SOqsRQvAz60P4Hr8h/OIAYevLNkSxZeDy+PjC41vp6PbR7fVzsO7E0hYf/6CMY40d/PHmM/j+R2by1v46rMJHTGz4RS7WEANHXSkA6TmFkJRDoqcp2M3U7fXxwxf38td3jwz8hu11PYu/jGBaaiC9cmv50ALC4W6iwcWgpUO50OyeOrWoDQSz0XqLQUfyZHWn3mjiFxQDu7qNSQRPR9/V0IZAvRHrCiXJZiXJZhm2GNQ6XcwrVGM8kbiBlBKTswITEhN+smmmaphjGSonFDMQQhQDZwAfANlSysC3rxbINu7nA6GOz0pj22DbK/vZrjkFaGhzk0YbQvo4Nu9/+KHnE8SUvwurvxS2X3lzJzkxXeTQFNWWwQ9W72F/bRs/vGoWADsrh55z7ujs5r41hzhncgYXTMvik8tK+Nst8wHISE4M2zc2RlkKTdXHAMgrKCIuvRAzftxONcN//3AT7W7v4OtENISk6o6gZbDbqMI90tCBs3MILUZOwDJQBWeShK7aoGVQZFgG6QnhFlRcYgoVZEPdHrWht2UQa/xfPSduHTS0uYN9iULJt8cNuwNvrdPF3PwUkmItJyQGzi4PWb6ez7FANEbMVTRkMRBCJALPAV+RUoY5J40Z/ahHr4QQdwkhNgshNjc0NBz/AM2oU9/mIluo2aMtvZBHfJdRXny9ym7x9lz0S5s6+a1tFf+w/SpyVZU+7wkFVV/YXsVTmyr4/PmTuG1pEfZ4KzsrHUM+/k9v7qfV5eG7V8wIuh4unKp83nlpSWH7BtxGXY0qLlFcVEJ6jkovLS9TDcpe26MuCi2dnoEvyI0ha0i0jlxa6u6qVuJjVPrrtorjWwcdzTW4zcYC9cezDDq7yaAVs98dtAzmFtixWU3kp8aF7ZsaH8NeXyFyIDGIMd7zJFxF9a2usOBxgDx73LAsg3a3lza3l1x7HJOyEk8ovbTa4aJQ9AhrPo0RyygakhgIIawoIXhcSvm8sbnOcPFg3AbOoAooDDm8wNg22PaCfrb3QUr5oJRykZRyUWZm5lCGrhll6lvd5FvUDzQlU328RxIXgbcLKj4M7lfT2MJCz1aKqKW+NQJmr98Pv58LG/40pN2llNy35hBz8lP42sVTEUIwJz+FHUO0DOr2vMs3Nq/g83PNzMhNDhmHcRE3WcP2txliIJ1V+DCRkJJFwQSVb19VfgSfX/LG3jpS49VxoW62MBoOQGyy+hshy8Dr87OvppWr5+dhErCt3DHgvrsqnXz675vYuGs/hz0ZeLAMyTLINzKJAmJw4YwstvzvxX3cRPZ4K/vlBGg6rNZ9CPRuCg0gwwmnl7a7vXR0+/q4iQDy7Daqh5FNFEgrzU2xMSkz8YQsg2pHFxNEPX6T+j9MMDdFrPBsKNlEAlgF7JNS/jbkqdVAICPoduCFkO23GVlFSwCn4U56DbhECJFqBI4vAV4znmsVQiwx3uu2kNfSRDn1bW4mxakve1JGAVazYKdlDggzHFO57x6fnzznNmKli1i66WptGuwlR4b2Omitgt3PARy3tmFreQvHGju4bWkRFrP6WcwrsHOwro2u7uMvXF+2ax2xwsOnC3tdkAM59+ZwMYizKTGwddXSZk4Dk4nsgmIAWurK2VLWQlNHN7ctVdsGFIPGA5AxFZJyRyxmcKShA7fXz1kl6UzNTho0bnDPU9vYWt7CrGQX/vhMnCQNyTLIF0YnUsNNJIQgISSrJ0BqfAz7/BNU6476fSFiYFe3QcvgxMQgUA+T3Y+bKDclDkenh87uk1uGNCAG2ck2JmUlUNfqps01tG6+NU5DDOzFkJjDlNgWKiNUeDYUy2AZ8AlghRBiu/F3OfBz4GIhxCHgIuMxwMvAUeAw8DfgbgApZTPwY2CT8fcjYxvGPg8ZxxwBXhmBc9NEgPo2F0VW5TU0JeeQlWSjvNMC+Qvh6FpAzXbOE9t7DopEla2jXN1Wb6O9qYoLf7OW370x8LKcz26p5Osxz3NN6Y+D2+YWpODzS/bWHN86cDco105a2/7wJ4KWQfiFzmaIQZZsojtOZcyIhCz8mHA3V/Lq7lpiLCZuP7sYIeDYQHGDhoOQOQ2ScoZsGVQ0d3LdA+8HL1q9CXTtnJWXzBkTUtle4cDfj5i6PD6ONXVw+9nF5JjbkAlZNMlE5HEsg6b2bvKEMSFIKRx039QEK/ulsU/dnqCbyG1N5J6ntlHaZqTsDOAm+uITW7njkQ/7+N37qz4OEEgvPdm4QaBGIWAZgBLYoVDtdDFB1GNOLwF7IUXmpuhxE0kp10sphZRyrpRyvvH3spSySUp5oZRyipTyosCF3cgi+oKUcpKUco6UcnPIaz0spZxs/D0Ssn2zlHK2ccwXpV4h5ZShvs1NnsUBcWlgMQrPnC6YeD5UbQGXk9KmTs43bcdjU8sYxnZEoP+Oo6dG4JX/PE5pUyfrDzf2u6vL4+OlHTV8LG4L1j3PBoumAtkgOyqOLwaWVpUbIWp3hT8RWMCml2UQb7iJ7KIDc7KRe2G20GFNw9xRx6u7azh3cgZpCTHkpcT1H0R2OaG9NsQyGNr/de3BBjaXtQRjEr3ZXdWKzWpiYmYiCybYaXN5+3V1HGloR0qYkpkIHfX4EzJVFXXvLqO9aOnsZqK1Wbm2At1HByA3JY5ymY3XHNcjBuZYNpZ38sL2arbWGv/fAcRg7YEG1h5o4NL73uWxjWVBUQuKQT+WQV5QDE5uRh5qGUzOMsRgiHGDmpZOJpgaEKnFkFJItmyIHjeRRjMYDW1usnCoixGQk2xTy19OPB+kH0rX01y+j0mmGrpn3QCA3ddIu/vkTPAhY4iBJzaduNI3SYq1sKfa2W/Pmdf21OJ2d5HTXQ7SB0feBtSPOSsp9rhBZJfHR5rbsHZqd4UHrQOWgTncFx4f1zMjjU/LC973JeSQKVuodrq4dFYOAMUZ8Rxr6ueCEKgvCFoGNUNKsdxrzPwHEsfd1U5m5iZjNgnOmKBKgfqLGwQCo9NS/eDrxpSURYtMxN9x/JhBkbmpZw3jQZiek0RmchwVliKo2x2sPn57vwpRNnsMke2nc2mby0O728sdZxezsCiVxhd/yLO//xqOzu6gm6i/AHKg1uBkq5BrWl2kJcRgs5qZkBaPxSTCxLTa0YXL07/rsdXRQCKdkFoM9kJSPXU4O920BtxM7/wSHrnipFJpj4cWA81J4/L4aHN5SfU3Q5Ka3WYlx6o21gVngjUejq4lrkxdXOOX3IFEkCuaRz+jqKUMmZDF6775LDfv5Dsrp+Dy+Ps115/dUsnZyU0IafxAD70RfG5ugZ2dx2lpfLC2lQJRj8earHL+W0p7nvT17yZKiOvJmolL7RGDmNR8skUzJgEXzVT/0+L0hP4tg0YjrTRjqloC0u8ZUvXv7irl1tt4pKmPOPr9kr3VrczOVwHaiRkJpMRZ+40bHKprx2wSTIhVY7Mm59AiE4cUM8gTjcd1EYGKJVw0I5tNrnyVUdTlQNpSeHO/Ks5r7DZEth/LINASYkFRKv/4SApfsT7Px52r+OXvf8vGo83EWEykxFn7HJeTYkOIk69CrnW6yElWgmI1myhKjw+KQXlTJyt+s5bfrenfZWlylKo7hmVglh4ycPa4ucreU5XmppG/dGsx0Jw0gbYSSZ5GSFSz2JxkGx3dPtq8AorOhqPvkN+4jkpTPiJzGt1xmeQwRDFoPgYvfgU8LraWt/Dxv24Yeqqno5wqMnmxcw5JdHJunPLp7+p1Ya9xdrH+cCM3TDC2586Dw28EZ17zClI42tDRMzPrh0PHSkkQbronXaI21O7seTLgNkotDjsmPi5kRpqQFbwbl5ZPjsnB4pK0YGZNSUYCzi4Pjs5eyzE27AdzrHrtJPX/P14Q2ePzc6C2jQlp8bS5vX2ypcqaO2l3e5mdmwzv/hpT00HmF9r7tQwO1bdRnB5PTJfKDIpLzcFBEhaXY9CU3qb2brL8DcHg8fG4aGY2u70FiK5maDyI25IY7ObZ0G2I7CBikJtiQ7x3H8JiozN1Ot90/5Fd+/aRlRRefRzAajaRnWQb0E20rbyFZT9/i6YB6mVqnC5lXXQ2g5RMylTppVJKvrd6Ny6Pn3cO9E2N9/slCR1GyVVqMdgnACG1BlJC9TbIX9Dn2JFAi4HmpKlvcyHwY3M3BS9GOYaJXddquIoaDzC9axsHks4CwJ+YS45oDraxGJQ3fwhbHqFuzzt85tHNfHismU+s+pB9NcfvweNrKWNbazLZ8y4Fk4W8hvXEx5jZ1UtM/r2tCinh7KRasNjgrM+patqa7QDMNeIGuwdJMa0rVzP0+DkfUVlUNTt6njz0hoqn5J0RdkxiiGVAYo8YiOQ8UmnjJx/pad5WnK4yZvoEkRsOQvpkntlazf+8ombK+w8dxDNI++VDde10+/x8+pwShID3ermKAsHjRZaD8NaP4ambWZxv42B9Wx9BPFTfzpSspGDBWVJ6Hi0yEZP0DJrd4+5wkuBvG5JlALB0YjrHzMXqQf1eGjzqO5Zvj6PeZbQC7ye1NNAfqIB62PkvWPRJ4m95nGSrn1XJf2NpiX3A98y12wZ0E721v54qR9eAK6LVOruYmNAFv54Ka37A5KxEypo6eXFnDWsPNDApM4H9tW009hKTxnY3+dJoR5JaFPz/5ItGJX7NR5WbLE+LwSnHobo2rrn/vaFd+E5B6o3qY5P0BsUgOzkgBm4lBoAVL/U5ywEwp+STMxQ3UeMh2PMfAF54+SX8UvLopxYTH2Pm1oc+4HB9G91ePxuONPGXd47QErqIud+HcFZSITO5ZukMmLAU0+E3mJ2X0scyeGFbNYuKUklxHoTM6TDlEkAEXUVzDXfJYPUGnXWHARCZ09Rr1BiWgd+vrIzJF4HJHHZMTEyIZZCY3XPf+D9OtvVc3IozVHVun/TSxgOQOZVnNley06n2eeTVDay8790B+9kELvbLJmcwOy+F9YfCxWB3VStWs6Co8iUV52g6wrX1f0BK2BkSSHd7fZQ1dTIlOzHYiiI5Mx8HRt7/ABlFUkriOw3rZQgxA1CtrDMm9ohpRWcM03OSmJ6TRFMXapz9iE+1w4UQkLXzL+r/f/aXIGMypst/xezuHfwqd+2A75k3SBXy9goHoFqy98bl8dHS6WG6pU657d67jwtca/D6Jd95fhczc5P55XXzANhwJDzQXu1UBWduW4ZKmTUsp0kxzSqIXL1N7agtg7Hn/SONPLTu6JD3f2jdMbZXOFh3sP9AXTRR2tjB+b96m0Mn0Iunoc1NlnCoB73EoNbpgqxZ+OLS6ZSxymUEWFPzyRXNx+8Xv/53SIsNhymVQtcBHvzEIpZPzeTxz5yFyST42AMbmP+j17npbxv5+Sv7eWh9yOfSWo1JemmJyWVegR2mXAx1u1ma6WZvTWvQT360oZ0DdW1cMTdXBSezZ0NChkqLPfQ6AKkJMUxIix/QPeXx+TEF0ljtRZA7t8dNVL1NtV6ecknfA0OL0ELFIFkF4kMzgwrT4jEJOBbS7A9PF7SU4UmdwvYKBxcvngvA9VPNHGno4L0j/X/n9lS3Emc1U5KRwLLJGWwtb6EjJJi/p9rJrGwb5r3/hhlXwTlfJffIM1xh3sjmsp4LfGljJz6/VNky7fUgTFgTM/AEWk4PEDfo6PaRJY36VMMNMhTOnj2ZKqmy0Uo7LFw4I4uUeKtqFT5A59Jap4vpCR2YdzwO829WcRWA+bfA9Cvh3V8PKFr5RhVy78RGv1+ywxCD0n6C+nVGYLrAZFzoM6axcOcPWSAO0tHt5ZcX25lf+yzTbM283+szqjEKznwpxkJHsUlgszMl1qHSS6u2Kus1c/qQ/mcnihaDE+D/Xt7Hz1/ZP6Qugq0uD6t3qAyTwExiOFQ0dwYXER8OpY0d/OjFvX1cCW8fqKe0qZPHPygf8mvVt7nINTnUg5CYARjLX5pMVE67g4d9K5mQpdoyiOQ8kkUnTscgLQ4c5cidT7M28TLWe6axPKGCxSXq+ImZiTz+mbM4Y4Kdaxfk8+AnFnLO5Az+vbUqmDboaSoFIHvCNEwmEbwYLzdvDwsiv7ZHmeSXFZvV7DZb9SNiyiUqLbZD/VjnFKTw7sEGbnpwI7c8tJHvvbA7KCiH6trJk3W4YjMgJh5y5qqCt7Y6Q1AETL6w7zmGWgohbqJAVlZoa4lYi5k8e6/00qbDgOQo+XT7/CyenAPxGZyR6iIx1sKru/pPG91T7WRmnsoUOndKBl6/5MNj6oIopWR3lZNrk4zirnk3wgXfgfyF/CJmFXv37Q6+zqF6NWmYkpUEHfUQn6HOKa5Xy+letHSEFJwN0U0EsGJ6Fvv9SjycMp4V07Owx8WoOEpMYv+WgbOLz1peVo3/ln2l5wkhYMX/qn5GH/yl3/fLTbHh9vr7/OZKmzpodSnx7M8yCMQpcgKC94l/Q3I+D8b8jrWpP2P2v87B/MrXuTfpdd47HG4ZVBliYE4r6dloL6TE2sz6Q41U7X0Pf86cPmnKI8W4FoPKlk6e21LJ79cc4hvP7OClnQMXQ+2raWV3VStevxy4ACiEF7ZV0eXxkZ0cO2wx2F7h4OLfvcPXn9lx/J2Pw4PrjvLwe8f6BAS3lKmL83+2V+HupwV1f9S3uploMywJwzKIizGTbLMEZ0gb8u7g194bgl0pSVY9CL3Ogf/X8r0/4JPwndoLyJy2hPiu6uCFGdTShH//5GJ+cs0cLpmVww1nFlLtdLHxqPpxHTusOlxOn25c3DOnQ3IBU1s/AAjO8l/dU8vcghRyXMrN0yMGFwMSDr8JwE1nTmB2fgpev582l5d/bCjj6c2qrmB3tZNC0aB8vKAC0KCsg0OvQ8EiiE/re5KBH7QlTs0AAyT1tQxABZHD3ERGg7oP2jIwCVhUnApJuZjb61gxPYvX99YOmCk0K0+1y1hYlEqsxcQ6w1X01KYKWjo9LHe/rS7uEy9Q4/zYQ8QKL+fWPxH0cx+qa8ckYGJmglr72BA0U4KavQ+0wE2TIQZ+kzXcIjoO6YmxOJPUimgeaxLzC1Oxx1vp6PYhY+L7FYNap4vlnnUw/QoIvcACZM2AGR9RYuDq6wLMG6DwbIfx3VGfR1/LIFBjYO+uhfh0SMnHdMvTpMaZmJDohwu/B9mzmWGuory5M6wYrq6lnTzRSExmyFhTJjDd5uDK2ZmkOvexuiEn2FF2pBm3YuDs9HDlH9fztWd28Ls1B3lpZw3feGYnlQMUeDy3paex6oHjuFKklDz+QTmz8pL52IIC9tW0DphXPCg+D6UNbXzq75twefysPzS8/HyPz88ru5S/NnDhDLC1rIWcZBuOTg9v7Rvagib1bW6KYoz/RcgPO88ex8u7arj/7cNsr3BgNQtyU4yAqWGqmweoQpZttfg2P8qznnO4/sIlnLXsIvVEwF/aD5ekN2C3CZ7dqj6j6tID+KVgwdw5agchYOJyEmo2kBgj2F3lpMbZxY4Kh8rlDzRBy56tbnPnQ0Jm0FV0zpQMnv7sUp753Nm88IVlLCxK5b41h+js9rK7yskEUwOxGaqvEDnGex5+E6q39u8igh43UWKmGl+AuFSVIdQrK6g4PYFjjR09bovGgyBMvFabxJz8FJJsVuViaqvhstk5tHR6gjP+AKVNHXR0+1SmUNVWbFYzZxansf5wA3988xDffn4XKyfHMaHhXZhzHQSW5kybSFfhci4wbWOtkd9/uKGdwrR4bFazsgwSVK+wmCRj/YHjWAaehNwTTo9MKp4PQG52DmaTwG70bfJa+l/TwOF0kuprVK67/jj3a0oINj3U56meRW7Cg8g7KpzEx5i5aEYW5U2dfdqcBCyDhK6aHssncxrmbx5F3P2+es/8hWS6jgIyLG7gbirFLCQiNdwyMLdWct+F8cQLN1u9Jdy66oOTbpUxGONWDP767hEcnR6e+MxZ7P/xStZ8TQU4f/ji3j77enx+/rO9ihXTs7CYBAdrBxeDbRUO9te2cfNZE5hfaMfrl8HAXZCmI8ctHPHefzbvPPhVpJT87No5dPv8rD80tG6tPr/s84V573AjLZ0eYsymMDGodnRR7XRx53kTyU6O5dkQ4RuM+jajSV1cKlh7iqj+79o5TMlK4levHeCpTRUUpsVjDqz0YYhBTK9FXEDNXN998leY/B4a5n2er140xZhpC+Uv7Y+yDcQ+dB7fy9/Kq7tr6XB76ao/isOSTnx8Qs9+JcsRXS1ckdnIrionrxsuopWzDTFIyoXArNZkgimXwsHXoMsR9nZCCL5z+XQa2tysWneMvZXN5IlGRMAysCVDaglsNZb6mHJx/+MO1B30nh0LoayMys1hm4vS42lzeWkJdC+t2YE/dSKbKjtZMtEYt1F4tnxaJjariVd6LYoTyH5Z7NsCf7sADr7OsskZHKxr5zdvHOSjZ+Tzp/nlCJ8b5t4QdmzSnMspEI3s3amaDx6ua2eKUV0bahnEpwQsg/7FoDlgGSQP3UUUYMaiC/BIM5OmKtEO1Ah4zPF9xKDN5SG125hwpE3s/wXzzoDJF8OG+/scP1Dh2fYKB7PzU5iYmUi3z9/n+VpnF0k2C5a2yvDU2VDhy5qB2dXC1ARXMLbj8fnpqjfiXqFpyCmFqqDuyFsAfP2Om/jzLQuIj+nbx2m4jEsxqG9z8ch7pVw1L4+zJ2dgs5rJt8dxz0VTeGNvHWv2hl+o3j3YQGN7NzctnkBJRsJxLYMnPignIcbM1fPzmT/BDvSq4Gw6An9aBHue7/d4ANpqsTQf5OzuDay640yuW1hAss3CmiHO2n/z+gHO++XbYVk2L+6oIclm4eNnFrClrCXoDgq4iBYXp/HRMwpYe7BhSBlQDYH21Ua8IMCCCak8edcS1vzPcu48t4TPLZ/U86ThBknsrgtzY7g8Pr705FYmVL5IWdIZfOFjl6occFsyZExRs+z+ePdXAFxg3klnt48/vnWYVE8t3uRewcmJSuwvsu1nb00r/91Zw5SsRNU7pm53j4sowFl3qR/hpr/1ecuFRWlcMjObv757lJbaY5jxh/+Ac+eCp1PVD+TM63/c5gHEAGDB7VD+PpR/ENxUkhGSXurpgqPvUJe5lG6fP0QMcqG9nngznD81i9f21Ib1FNpTrTKFCh2b1IYdT3LRjCxiLCbuPLeE31w/D8uuZyB9Sp9UWGFYOAllb9HV7eNoYzuTs5JU7ntHfVAM0pMTcMp4vO39t6QINKkzpw09eBygYOIMuu/ZzYILrgXAHq/qMNymuD6ppbVOF8XCEMPUXi6iUM77ugryb3k0bHNaQgyxFlNYrUG318/e6lbmF9qDbs+yXq6iGqeL3ORYcFYOHBMxAsAfyXXy/pEm/H7Jt57diaXViNeFfpcCgrL3BYhNJjl/OmdPyhj4fIbBuBSDP799hG6fn69ePDVs+6eWlTAlK5Hvr94TNqt+dksl6QkxnD8tk6k5SRwYxDJwdnl4aWc1V83PJzHWQlaSjXx7HNtC4wYVH6pWDRUfDPg6LYfVD3aKqGRBqhur2cTyaVm8vb++36Zhobg8Pp74sJzG9m7uMyodXR4fr++pZeWsHJZPzcLt9bPdEKgtZS3EWc1Mz03iuoX5+PySF7YN3kyurtVFY3s3GTh6Cp56MTkrke9eMZOPLwr5UcTE47amkENzMDjn7PRw26oPqd69jhJTHcUrPqUCvwHyFig3Ue9CpqqtcORNiE3GXreBktRYHnz3CAWigaScXrPBpBzInM5czw5cHj8fljYrF5HPo/zvvcUgd55y8Wz4c78uiG+unEZnt5fswEIkAcsgcCwYKaUD/MSCbqKsvs8t+qQKxK77dXBTsSEGpY0dxloRXbxvPhOzSah4QeAcURfny+bkUN/mDqsc3lPtZGp2EuaKDWrDgZeZkiLZ+f1L+O4VMzG1HIWy9coq6F2MlZJPW8o0zvZv5dktFXh8UlkG7jbwuoKFc+kJMbTIJLrb+rdgm1rbycKB9STEACAhLS84NrthGbiErU/MoMbpokgYk7re8YJQJiyB4nPhrZ8oQTC+Y0KIPovc7K9tpdvnZ16BPVj70Tvdt7bVxeQkj5oMDCQGWTMAWJZcT0Obmy89uY3nt1VxXYlXpckG4kbQk3FVuUl9r0ah8jjAuBODypZOHv+gjI8vKgjOtgLEWEz85JrZVDm6uPe5XZQ2dtDS0c2afXVcc0Y+VrOJ6dlJlDd3Duiz+8+2KlweP7ec1fNlnz/BHrzwAj3+79DipF5U79/Y8+DYuwBcNCOLpo5utoekOe6ucvJBL///K7trcHR6mFdo558flHOwro21Bxpoc3v5yLw8FhenIQRsPKpM+a3lLcwrTMFqEkzOSmJ+oZ1nt1T2SasLJeBmSvU1DSgGA9Edn02OaAmml/769QNsLW/htzMOgMWGmHl1+AH5C4yW1L0Eat1vVF/7S36CcLfy2SlOTNJLrmgmLrMf10DJcjKbt2BFfXYrZ+eoegZfd0+8IJTzvqHcHZsf6fPU5KwkbjizUAWPIXw2F5hVTx0gXgDqR2+O7T/XPiYBlt6tYhbGd6QwVaWXljZ1wMFXICaR55qKmR2IFwAkGamTbTWsmJ5FjLnHVSSlZE91Kwuyreo1J56vLuL7XlR+f4B1v1Wpiwtu63fItpkrWWQ6wBPvqqyi0BqDgKhlJMbiIBHfAJZBa10pJiERJ5BJNBCBmEEncX0Eu8bZRbGow2dLVW7Mwbj2QShYCC9+GZ66Wbm9MGoNQtxAgZTSeYUp5CTbiLGY+rUMptscxgAHOMfEbLDZmWpSy7b8d1cNty6ZwIJkp0pPDr3gp4SI5ijVFwQYd2Lw+zWHEELwpRVT+n3+rInpfG75JF7aWc35v17L1fe/h8cnuW6h+tFOzVGZH4fq+q+wfHpTBbPykoO9XQDOKLRT5ejqKbQyXB6ydldwvdve+Ku2U04OMi412Ar6/KlZmE2CN/epGU+t08UtD33AbQ+Ht+h98oMKitPjefj2RSTEmPnxS3t5cUc16QkxnD0pnZR4KzNzk9lwtJHObi97qltZmdEMPyuAys1ct7CAA3VtgxZabTzaRLLNhLWr4YTFwJ+UR45ooqHNTWe3l39vq+KjczMpqXkVpl2uXEOhBCouQ11F9ftg/0uw+LMqKwTByrh95Ism5bbpL4d94nJM3i6Wxhwh3x6nsmqCweNZffcvXKxmje//ETwu8HYrt9STN4PHxXcun8Gds4Xy/yeHrNQ68QK4+RmYcXXf1wxgtsCnXoEz7+z/+TPvVF091/0GUBOV/NQ4jjW0w8HX8JVcwObKTpZMDMlUCnwOrTUk2aycMyWD/+6s4bU9tWwpa6G5o5vz4o+pZnxnf0n50nc+pY5pKVP3F94R7DPVG+v0y7AKH8VOFTeYlJnYs9ylEUDOSIqlZZA21i5jZbehtqIYDHucchN1MIBlYKpFpE/q79BwkvPgEy/Apf+nAv+PXAZSkpsS3pJiW4WDjMQY8u1xmEyCorT4sHTfbq+fxnY3JdbjtOcWArJmkNh6mMXFaVwzP48fXjUb0XQk3MIElYlmNTLxRqnyOMC4EoNWl4dX99TyiSVFwdSx/rj3suls+PaFfHPlNCwmwbLJ6cHVq6ZlKzHoz1W0u8rJ3ppWbjgz/Esw32hpsL3CAT4Pvpqd1Es7wtOJu3Z/n9eRUpLRvp+6pJmI4nOVW0BKUuKtLCpK5c19ylX0jWd30O31YzYJfrB6D1JKDtW18WFpMzctnkB6Yiz3XDSVdYcaeWV3DZfPyQ0u3LJ0Yjpbyx18eKwZn19yntykflDv/Z6r5ueRFGvhwUEWYt9wpIkVEywIv6dPzOB4mFPyglXIL+2sod3t5c78Y2oW3itwCUDObHXBDQ0ir/stWBNgyefVDyZ3Hvaa9Tx2nWFi9/5RARQtA2Hii8VVfOWiKSomUbdbuWzS+58ccN7XVZvo1/8X/nKOcicc+C9s/ydJNiuTrU3qRx9aNyCEsgqOZ9LnL+wrfAHi7LD4Tti7OphGOic/hfI970NbDdviloTHCyAkLVVlIn1iaRGN7W4++9gWrvuLcg3N9u4GYYLCs9T/+tg6cFbB+t+p7cvuGXi8BWfitiRxgWk7+fY4tRhNu+GKSQxxE5GEydU3tdTj82NpNZITRsAySLJZEALa/bHKyvH1WOu1ThcTTfWYBgoe98ZkgqVfgEt/Ck2HoKWU4gy1MM0L29UMfkeFg3kF9mA/o6L0hDDLoL7NhZSELNwziCssczrU7+Ppu87ivhvPwNzZCHW7oHBJ+H5C9PyvtGUwciTbrKz9+vl8acXkgXfydsO635B9+BnuPn8yb339fB7/TM8HpNLpTP0GkZ/eVEGMxcTV84xZorMSXvgis9PBYhJsr2iho2InZp+b1SZViPSXJ5/r0/DqWEUFuTRiyT9DBT5bK1VfEuCiGdnsr23jZ6/sY92hRv73yhl85aIpvLm/njf21vHEh+XEmE1BS+a2pUVMzEzAL+Ej83q6Yy6ZmE6318+q9WpB9kKnkb2y/yWSu6q57ewiXtld229Fco2zi9KmTs7LMayaAWaSAxGbVkimaKXR2caTH5YzOSuRKbUvq7zs/gq0rHHKzxpwr+16FnY/q3zrgRz+SRdA5SYmdKvzwd6PGMTZIe8MFstdXL+oUH3Wx95RXT8tMX33ByhZDvmLVCDZ0wW3PKsupOvvU8e3lPYvPCPBkrvVub/5I5CS//voHL5WdAyfFNy1MV3FC4pCXCAJGao3klGjcMG0LHb+4BL+84Vl/PzaOXz38hnkOLYp33NsEsy5HpDw/h9g++Nwxq09Vbr9Ybbgm7iCC8zbmZplzFYDbiIjZpCZFItDJhLT7ehzeEVzJ3kY4jHEVhSDYTIJkm1WWv1Gaw9Pzyy93tFKNk2Dxwv6Y4LxW6/cxCeWFrG4JI17ntrOfWsOcqShI7jGBUBxejxlzR3BGN5Ow5LOo1FNVAZzT2XNAJcD0WFYVka2UL/ff/sEo2Zh+AI6GONKDEAVrwSyEPpQvw8eulD9+F77jnIN9MJsEkzNTuJgr4uky+PjP9uruGx2DimGL5NXvw3bHsN24D9Mz01iW7mD195Qi7gtvfZufGYbdudePvrn98PcPId3vA9A3vSzoOR8tdFwFV04Q/3o/rbuGCumZ3Hz4gl8clkJ07KT+OGLe3l+axWXzs4hPVH9QKxmE7+6bh63LpkQduE4syQNk4B1hxqZkRmDtepDmHkNIODDB/nUshJsFjN/XtvXOvjAiDUsSDMylUIDXkPAmqouBHsOHGBbuYNPnGFH7H8ZZn9s4OrKvAXKTfTMJ+G5T6tZ9blf63l+4gXg98L2J9QFMdRtE0rJclVd3NWiXqd6m5oRDoQQcNUf4eIfwxc2qlTR874JzgrY8aRyr/QnPCNBQgYs/5Zyh73/R+zxMSxnM968RZx/xgxuOWtCT7wAlHXSa8Wz+BgL8wvt3Lh4AneenY+o3AwTVGsQ0idBwWJVeCX9cM5Xjzuk+FmXkymc3FJkuBDb6wGhLlaoPkId5mRifB1KLEM40tDBxaatdGTMA0vfdQROBnu8lRav8VqhcYOWMuUuHKplECBrprqQV3xIss3KPz61mCvm5HLfmkNAj5UPUJSRgMvjD8a+Xt5VQ3pCDNn+euUG66cjapBAS4n6fer28BpV6Jc7v+++y7+pvoODvd4IMO7EYEA2Pwx/Xa6ClMvuUT3DD70Wvk/1Ntj+JFOz+2YUvbq7ljaXlxsCmTPH1sG+1YCAvS8wv9DOptJmXGWb6TInMWv2Qsy5c/lYbhMtHd189z+7gwHbtmNqlp499Uz1g00uCK4nPDEzkYmZCaQlxPDzj81BCIHVbOInH1WBb2eXh5sWh88gFhal8pNr5oRl6KTEWZmVp+IaH82oAp8b5t0EM6+GrY+RbvVwy1kTeGF7VZ+y+w1HmkiJszIhxrggnEAlKRAMdNZVHlVWjG2zev/+XEQB8s5QBUL7VqtWAp98Nbyyd8ISVc1btwtS8ntSN3szcbkSjb9fqV7r0p/BGbcMPt7smbDsyz3r7U6+UI3nnV9CZ2Of9tQjyrJ71Gey5vuw7Z9Qs4PYWVfw2xvm86Or+wl6J+UMvKxo9Tb1fzb6RAEw9+Pqdt6NQ+sVNPkiQHBR22rllumoV0IQ8v/2xtrVnV5VyC3HtjHTVIZp/o3Hf58hYo+z0uI1BDEkvdTWZqRpnqgYmMzKHVOp4iI2q5k/3nQGnz6nhHx7XB/LAFRQ3+Xx8db+ei6dnYNwVhx/Fm9kFNGwX9UbHXlTfa/6cy0WLlZV1KOMFgOAD/8GL30VSs6DuzfAhd9XZu+uZ3r2kRJe+CK88AXmpXqob3OH5fA/vamCwrQ45cP1++DVe1UmwJK74dg6zsoWeHySxTGlxExYoFQ+bz6JLXu558JJvHtQLc/n9fmJb95DszVXXeyM6lmOrQsWqT1wy0KeumtJ2PqtZxancdvSIuYVpLA01I/cH8aMLRB8PMeyV82mi85W43U7YceT3HneRCxmEw/0sg42HmticUkapoC/+AQDyAFXRI5o4YrZGSRs+hNkz1Gz/YGYcZXKcvnMmyrLp/fF3tLTDG/QmXrhWSqLp263+pyX3n1iYwf1mZz3TeW+g9FzEwXe6+o/Q+YMeMGwYKZeNvD+gy1/Wfaeup2wtGfb3I8r99D53x7aeBIy4MxPw/Z/wiMr1XoNvdJj/YH+RL0KzzKP/gcvJuLO+PjQ3msIpMTH0NwdWO1MiUGby0O2V/n5T1gMQF18a3cHLQ2TSfD/rpzJ+m9dELYYTiC9tKypg3cPNtDZ7eOy2TnKajxegDwhU6UP1++DGqOh4eSLTnysI4gWg62Pwctfh2lXwE1Pqi+2yaxcFgdf76lAPbxGXUCkj7O6VTAuEDcoa+pgw9EmPr6wUM2+tz6q9r3kR+rHJn0slx+yuCCOSbICc+CilzsPutu5bZqPiRkJ/Pi/e9le4WCa/yiuzJBZX8ly9cOqUwulTMtJYmp2SD8bgx9dPZsXvnhOvwt2BNnxNPyyBJqPcumsHJJtFiZ1bFMzXVsyFJ6pfOQbHyA7MYYbFhXy3NZK1abD76P1tf8juXm3Er22WpXaaR04GN8vQTFo5otpm1Q8ZMV3BzeDE9KVqZw3f+B9Jl2gbgcTA2ucar522S/h3P85sXGHMu0yJWAwupYBQGwi3Pg42OyqgCpz2sD7JuUo67a/tOCyDco9kRAyWbClwNX3n5gP/4rfwMdWqZYYVVuCmUQBTAGLLTSjyO9nbsvr7IxdpARlhLDHWWkMLn2pLt51rarGwGNJDLqvToiCxSrjqnp72GYhhJqQ7X8ZXv9fcuP9WM2C0qZOXtldiz3eypKCWGURHc8yMDKKaNhv9MASMGnFiY91BBnfYrDrWVj9JZh0IVz/SLi/es71yqTe/5J6vP4+5YdOm0hRrXIfHaxTPfX/9z+7sZgE1y0qUOLx1k+UX3bmNeqCby8i+ejL/OvqJIT09mQFGP7BmLqd/O+VMzja0MEPntnIRFMtKRMX9Yyl5Dx1e/Sd4Z2vlLD+t2oG9c4vWVScxs5vLyO2dmvPe4DK0Gk+Atsf53PnT8JsEtzxyCba3vwNyRt+weMxP+WCpEqVZXOC8QIAbCl4zXGcndbGxL33K4tg6srhnRuouAEc391xzlfgrM8O772EgIt/CFmzIGOQi/NIkVYCn3oVbnhscNHMnac6jj51S3grDb9PFTmGuoiGw5zr4PMb1KRp1jVhT1kSjYt9iGUgS9eR7m/iYPblI/P+BvZ4K/XucMug2uGiWNTRnVx0cn72gjPVreEqAtT/b+tj8Oez4Kmb4P0/Ytn2DwpT4zlU18aavXVcMjMba5thkQzF5ZY5Her3q7Uz8s4YUZE8GcavGDgqlNun6Gy44Z99A1r5C9QsbNczqk9M2XoVaJz9MWIq3qPE1sm+mja+/swO1h1q5GfXzlHN2N75pZoRXfZz9UUUQvl8j66Fo2ot4GBRUuY05bKo2c4F07I4d0oG8c2qN1JCUYjLJDlXFUVtfyIsfQ6AV78DT986YL1CGEffVjORjGmw82mVsli+UfnQQ8Vg5jUqv/6/XyO/fQ9//+RiUh17iHvv52y3LaZNJFLyyq1QvePE4wUAQmCx57Oi6w2Es1LFAEYiOJY9S834jxcDGCkmXwh3v69m7pEga0ZPI7yBOOMTsPIXKt711/PU9+7IWyp11N3aEzweCVLy4bqHYdGnwjbbUpSl4OsIacK29UnaZBzuSZeO3PujLIM6t5HWa1gGtU4XE0Qd4mRcRKAsp7RJULGpZ9u638DqL6qivI+tUmnK7/+BSWkW3jYKOi+bk6uuKzC0zJ+sGcolW/nhmLuIYDyLwZrvAxI++hfVh743Qijr4Ni78Pr/Uyb6gtth1kcR0s/NyTt4ZnMFq3dU882V01SqYsNB+PCvyrcdaEkA6uLq98CGP6lYRCDTxWxVOfQ1OxBC8L0rZzLHbBTlhB4PKqOgYZ9KAQxQuh423g/7XgwWJwU5vAbe/r9wkdj4gDLpP/FvFWxd+zMVmDbHKF96ALMFrn9UuRyeupklyS08lvoQTaRwu+Mz3D/hdwhrPDjLT84yAOUq8napH1VgRj9chFAz/hFIWzxlEQKWfA4++YoS+X9cDY99VC1hGZca7NE0miSmqBlul9NIO/V0YT3wIq/6zqQ4N3OQI0+clPgYOvxG7MywDGod7RSKBmKzBkkhPx6Fi9VFWkrl9nn/j8qV/Fmjo+u5X4O2Gq7iHXx+SZLNwrJJGSpeAEMrqgtdpGaghoYRZHyKQdkG2P2cytQYzJybc51KuSt/XxUAxSaq1LP0KVzofw+vX3LH2cV8PtCI7fXvqmrBFf8v/HXyF6iMIJdT3Q+dBefOV+0B/H6mZCfx5Zmd+BOz+/asmXGVumC//VOVNeHzwH+/rsY/66Ow9uc9jc32/BueuAHe+QW88T21rfGwam9w5mfUjG7J59V+u55RPtLegpiQDjc/rWZbfzkHm/Mo7o/8mZzsXM5bfCbc/qIStYHaAx+PQOuEC44TK9CcHIWL4XPr1Sz2jv/CV/fCN4723wtphElJseOWVtytRvHVgVcwe9r5t/8cJmUmDH7wCWKPs9KBEbMyLANXYxlW4cOcMYTq44EoWKRqKFpK4f0/Kavqgu/0fFcnrYC8M1je8ARmfFw8I5sYi0mJgck6tELMQEaRzT548kSEGPk+qNGO3w+vfktdyAartgTlxsmZqwJliw0fsxAw66OUrPs1P7koi5tXzFSBpYOvq4vtJT9RPepDEQJmXgUb/9y3pDx3HmxeBS3HIDaZlKYd/ecaCwGX/BRWXaRmKbFJylK48UkoPkdV5z73GTjnHnj5G0o4MqcpayR9smq7YI7pMenP/qLKomqrUS0I+iNrhrqYPHUTnP0lJiy8jNdCv7Nf2dVnbd8hs/AONb7iZSd3vOb4xKepCU2EyUiy0UIitDcqV+Sr9+KIyWG7bzZ5KSeYbHAc7PFWOonFb7JiqlUJFsIo0DypTKIABYvV7cFXVR3GrI8qKz6AEHDu10l++hauNG3girmGZe2oUJOtoTSUS8hQ16Gis0/+dzSCjD8x2P64molf+1BP3vhgfOT3aoYQeoGf9VHEu7/k1pQdYDpTzdJf+47yMy4eIDA55zr1pSo+J3x7IDvmbytU4A9Uznd/FJ6pvpTv/0G1DphyqcpqEUJdtB++FP77NeXvv/lpFY9wVqltZqtyewVmhnGpqj/N2z8JLlzfL9NWwtcO9MkYAYb3BS5aqv40px0ZiapzaV7DTnjkcjCZ+WX6Tyh2JYd3ox0B7PFWfJipmnobhTtWwfybsbUZrtYTrT4OJVB89uaPVOX58nv77jPtcmTmDH7ueQPbtJ+obUOpMQjl9heP30gvQowvN5GrVX24hWcNfcaUvwCm9gp6Zc1QQdiND8CTN8HvZql+Jit/NnBbg/yF8I0jfWfCWTPV4tyTLlBWxR0vD14JeuH3lfj4PD1BalBCceVvYf6tcPO/lNCZLSrAlzlN9W4563Phr3XOV+DW58LjBf2RmKVdOZohk56omtWltB5U1ugdL7POmc6krJEPtAfy/ndMvhvSJiJXf5nsrsN4ROwJ98wKw2xRv31Pp7pWZPWzCL3JhDjv68Q5DiH+c7fqWOA4QTFIn9T/sqhjwPiyDGISlN8vb/7wLm5CqIyVN74HCBUAnXzhwEscBhhoLdwbH++7fSDSSlTQ22ztawYvvKOvy8eWDLetVmvy9vbvm61RkcWgOb1ItlkoJY+ZsS3YP/kyrqQJVLYc4GMLRj6wn2J0Lm32WOCqPyL+fgUfo5S2hEnYh9v7v2gZlL3fv1UQYPbH1GJVa/9PpWO31YxIR9axYHyJgcmsmpuNBGd/WbmEQpZ7jBgn6gdOzOy/AZZGMwoIIfhz3GfZXpLKL9NKOFbTipRGy+sRJmAZODo9UHwO5RNvZMLRp4bWuvp4LLsHZl8LGYNkJQkB539LWQ7//hwgR72h3GgxvtxEI4kQYyMEGs0pQGpSPPVdqgr6SINK+RwNMYixmEiIMSsxAFZnfpbD/jzip5w7Ai8eP3i1dygzr4ZPvaZuT9GJ1/iyDDQaTUTITIrlvcON3PDXDXR2+xCCPisLjhT2+BicXUoMttf7eSHlAd445/xRea9ByZ0LH/9H5N93hNBioNFoRpwvXDCZzMRYjjS0U9HSyZlFacTFjE76ZEqcFWeXar64r6a1Z01ozQmhxUCj0Yw4C4tSWRiyfsZg62kPF3u8FUenB0dnN1WOLm7LHcUusqcxOmag0WhGnUE76Q4Te7wVR5eHvTWtAMzMG2ApUc2gRI0YCCFWCiEOCCEOCyEGyeXSaDSaHlLiYnB0ethbrcQgsF655sSICjEQQpiB+4HLgJnATUKImWM7Ko1Gcypgj1cxg73VrWQnx5KRODJLao43okIMgMXAYSnlUSllN/AUcPUYj0mj0ZwC2OOseHySzWUtzNRWwUkTLWKQD1SEPK40toUhhLhLCLFZCLG5oaEhYoPTaDTRiz1eFZ6VN3fqeMEwiBYxGBJSygellIuklIsyM0e2L7pGozk1CV2XeGZuyhiO5NQmWsSgCgit4S4wtmk0Gs2gBPoTgc4kGg7RIgabgClCiBIhRAxwI7B6jMek0WhOAQJuovgYM0Vp/axaqBkSUVF0JqX0CiG+CLwGmIGHpZR7xnhYGo3mFCAgBjNyR369hPFEVIgBgJTyZeDlsR6HRqM5tbAbbiKdSTQ8okYMNBqN5mSIizHzzZXTuHB69lgP5ZRGi4FGoznlufv8QdYc0AyJaAkgazQajWYM0WKg0Wg0Gi0GGo1Go9FioNFoNBq0GGg0Go0GLQYajUajQYuBRqPRaNBioNFoNBpAjOZC1aOJEKIBKDvJwzOAxhEczqnAeDxnGJ/nPR7PGcbneZ/oORdJKfvt/3/KisFwEEJsllIuGutxRJLxeM4wPs97PJ4zjM/zHslz1m4ijUaj0Wgx0Gg0Gs34FYMHx3oAY8B4PGcYn+c9Hs8Zxud5j9g5j8uYgUaj0WjCGa+WgUaj0WhC0GKg0Wg0mvElBkKIlUKIA0KIw0KIe8d6PKOFEKJQCPG2EGKvEGKPEOIeY3uaEOINIcQh4zZ1rMc60gghzEKIbUKIl4zHJUKID4zP/GkhRMxYj3GkEULYhRDPCiH2CyH2CSGWnu6ftRDiq8Z3e7cQ4kkhhO10/KyFEA8LIeqFELtDtvX72QrFH4zz3ymEWHAi7zVuxEAIYQbuBy4DZgI3CSFmju2oRg0v8DUp5UxgCfAF41zvBd6UUk4B3jQen27cA+wLefwL4HdSyslAC/DpMRnV6PJ74FUp5XRgHur8T9vPWgiRD3wZWCSlnA2YgRs5PT/rvwMre20b6LO9DJhi/N0FPHAibzRuxABYDByWUh6VUnYDTwFXj/GYRgUpZY2Ucqtxvw11cchHne+jxm6PAteMyQBHCSFEAXAF8JDxWAArgGeNXU7Hc04BzgNWAUgpu6WUDk7zzxq1ZG+cEMICxAM1nIaftZTyXaC51+aBPturgX9IxUbALoTIHep7jScxyAcqQh5XGttOa4QQxcAZwAdAtpSyxniqFjjdVhC/D/gm4DcepwMOKaXXeHw6fuYlQAPwiOEee0gIkcBp/FlLKauAXwPlKBFwAls4/T/rAAN9tsO6xo0nMRh3CCESgeeAr0gpW0Ofkyqn+LTJKxZCXAnUSym3jPVYIowFWAA8IKU8A+igl0voNPysU1Gz4BIgD0igrytlXDCSn+14EoMqoDDkcYGx7bRECGFFCcHjUsrnjc11AbPRuK0fq/GNAsuAq4QQpSgX4AqUL91uuBLg9PzMK4FKKeUHxuNnUeJwOn/WFwHHpJQNUkoP8Dzq8z/dP+sAA322w7rGjScx2ARMMTIOYlABp9VjPKZRwfCVrwL2SSl/G/LUauB24/7twAuRHttoIaX8tpSyQEpZjPps35JS3gK8DVxn7HZanTOAlLIWqBBCTDM2XQjs5TT+rFHuoSVCiHjjux4459P6sw5hoM92NXCbkVW0BHCGuJOOj5Ry3PwBlwMHgSPAd8d6PKN4nuegTMedwHbj73KUD/1N4BCwBkgb67GO0vmfD7xk3J8IfAgcBp4BYsd6fKNwvvOBzcbn/R8g9XT/rIEfAvuB3cBjQOzp+FkDT6LiIh6UFfjpgT5bQKAyJo8Au1DZVkN+L92OQqPRaDTjyk2k0Wg0mgHQYqDRaDQaLQYajUaj0WKg0Wg0GrQYaDQajQYtBhqNRqNBi4FGo9FogP8Pm00FQRAMdiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Elastic Regression\n",
    "from sklearn.linear_model import ElasticNet\n",
    "en = ElasticNet(alpha=0.2)\n",
    "en.fit(ss_train_x,ss_train_y)\n",
    "en_pred = en.predict(ss_test_x)\n",
    "ss_en_pred = ss_y.inverse_transform(en_pred)\n",
    "print(\"mse:\",mean_squared_error(ss_en_pred,y_test))\n",
    "print(\"r2_score:\",r2_score(ss_en_pred,y_test))\n",
    "mean_squared_error(ss_en_pred, y_test)\n",
    "plt.plot(list(range(len(ss_en_pred[:100]))),ss_en_pred[:100])\n",
    "plt.plot(list(range(len(y_test[:100]))),y_test[:100])\n",
    "plt.legend(['pred','true'])\n",
    "plt.show()\n",
    "plt.savefig(\"en.png\")\n",
    "mses.append(mean_squared_error(ss_en_pred,y_test))\n",
    "r2_scores.append(r2_score(ss_en_pred,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model fine-tuning\n",
    "### Best svm\n",
    "#### Search the best svm regression model by using grid search alogrithm.\n",
    "#### R2 score and mean squared score  are used to measure the performance of the best model of svm. \n",
    "#### Select out the first 100 test labels and predict labels for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.1, kernel=linear; total time=  34.4s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.1, kernel=linear; total time=  36.4s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.1, kernel=linear; total time=  33.0s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.1, kernel=linear; total time=  34.2s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.1, kernel=linear; total time=  37.3s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.1, kernel=rbf; total time=   2.7s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.1, kernel=rbf; total time=   3.0s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.1, kernel=rbf; total time=   3.0s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.1, kernel=rbf; total time=   3.2s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.1, kernel=rbf; total time=   3.1s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.2, kernel=linear; total time=  37.8s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.2, kernel=linear; total time=  39.6s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.2, kernel=linear; total time=  37.2s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.2, kernel=linear; total time=  37.4s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.2, kernel=linear; total time=  36.5s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.2, kernel=rbf; total time=   3.3s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.2, kernel=rbf; total time=   3.2s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.2, kernel=rbf; total time=   3.1s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.2, kernel=rbf; total time=   3.3s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.2, kernel=rbf; total time=   3.4s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.3, kernel=linear; total time=  36.1s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.3, kernel=linear; total time=  38.4s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.3, kernel=linear; total time=  34.1s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.3, kernel=linear; total time=  36.7s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.3, kernel=linear; total time=  39.4s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.3, kernel=rbf; total time=   3.5s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.3, kernel=rbf; total time=   4.0s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.3, kernel=rbf; total time=   3.6s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.3, kernel=rbf; total time=   3.6s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.3, kernel=rbf; total time=   3.5s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.4, kernel=linear; total time=  35.6s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.4, kernel=linear; total time=  38.5s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.4, kernel=linear; total time=  37.1s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.4, kernel=linear; total time=  37.8s\n",
      "[CV] END ..........C=10, coef0=0.1, gamma=0.4, kernel=linear; total time=  38.4s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.4, kernel=rbf; total time=   3.8s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.4, kernel=rbf; total time=   4.1s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.4, kernel=rbf; total time=   4.2s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.4, kernel=rbf; total time=   4.0s\n",
      "[CV] END .............C=10, coef0=0.1, gamma=0.4, kernel=rbf; total time=   3.9s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.1, kernel=linear; total time=  42.0s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.1, kernel=linear; total time=  41.7s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.1, kernel=linear; total time=  42.4s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.1, kernel=linear; total time=  41.0s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.1, kernel=linear; total time=  41.1s\n",
      "[CV] END .............C=11, coef0=0.1, gamma=0.1, kernel=rbf; total time=   2.9s\n",
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      "[CV] END .............C=11, coef0=0.1, gamma=0.1, kernel=rbf; total time=   3.0s\n",
      "[CV] END .............C=11, coef0=0.1, gamma=0.1, kernel=rbf; total time=   2.9s\n",
      "[CV] END .............C=11, coef0=0.1, gamma=0.1, kernel=rbf; total time=   3.3s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.2, kernel=linear; total time=  42.8s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.2, kernel=linear; total time=  41.4s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.2, kernel=linear; total time=  46.9s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.2, kernel=linear; total time=  53.0s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.2, kernel=linear; total time=  51.8s\n",
      "[CV] END .............C=11, coef0=0.1, gamma=0.2, kernel=rbf; total time=   4.1s\n",
      "[CV] END .............C=11, coef0=0.1, gamma=0.2, kernel=rbf; total time=   4.5s\n",
      "[CV] END .............C=11, coef0=0.1, gamma=0.2, kernel=rbf; total time=   4.3s\n",
      "[CV] END .............C=11, coef0=0.1, gamma=0.2, kernel=rbf; total time=   4.2s\n",
      "[CV] END .............C=11, coef0=0.1, gamma=0.2, kernel=rbf; total time=   4.1s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.3, kernel=linear; total time=  52.7s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.3, kernel=linear; total time=  50.6s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.3, kernel=linear; total time=  51.1s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.3, kernel=linear; total time=  51.5s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.3, kernel=linear; total time= 1.4min\n",
      "[CV] END .............C=11, coef0=0.1, gamma=0.3, kernel=rbf; total time=   9.7s\n",
      "[CV] END .............C=11, coef0=0.1, gamma=0.3, kernel=rbf; total time=   8.5s\n",
      "[CV] END .............C=11, coef0=0.1, gamma=0.3, kernel=rbf; total time=   7.8s\n",
      "[CV] END .............C=11, coef0=0.1, gamma=0.3, kernel=rbf; total time=   7.5s\n",
      "[CV] END .............C=11, coef0=0.1, gamma=0.3, kernel=rbf; total time=   7.7s\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.4, kernel=linear; total time= 1.5min\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.4, kernel=linear; total time= 1.4min\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.4, kernel=linear; total time= 1.6min\n",
      "[CV] END ..........C=11, coef0=0.1, gamma=0.4, kernel=linear; total time= 1.4min\n",
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      "[CV] END .............C=11, coef0=0.1, gamma=0.4, kernel=rbf; total time=   8.6s\n",
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      "[CV] END .............C=11, coef0=0.1, gamma=0.4, kernel=rbf; total time=   9.4s\n",
      "[CV] END .............C=11, coef0=0.1, gamma=0.4, kernel=rbf; total time=   9.3s\n",
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      "[CV] END ..........C=12, coef0=0.1, gamma=0.1, kernel=linear; total time= 1.4min\n",
      "[CV] END ..........C=12, coef0=0.1, gamma=0.1, kernel=linear; total time= 1.6min\n",
      "[CV] END ..........C=12, coef0=0.1, gamma=0.1, kernel=linear; total time=  53.0s\n",
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      "[CV] END .............C=12, coef0=0.1, gamma=0.1, kernel=rbf; total time=   4.9s\n",
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      "[CV] END .............C=12, coef0=0.1, gamma=0.1, kernel=rbf; total time=   4.3s\n",
      "[CV] END .............C=12, coef0=0.1, gamma=0.1, kernel=rbf; total time=   3.8s\n",
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      "[CV] END ..........C=12, coef0=0.1, gamma=0.2, kernel=linear; total time=  50.9s\n",
      "[CV] END ..........C=12, coef0=0.1, gamma=0.2, kernel=linear; total time= 1.1min\n",
      "[CV] END ..........C=12, coef0=0.1, gamma=0.2, kernel=linear; total time= 1.0min\n",
      "[CV] END ..........C=12, coef0=0.1, gamma=0.2, kernel=linear; total time=  59.1s\n",
      "[CV] END ..........C=12, coef0=0.1, gamma=0.2, kernel=linear; total time= 1.1min\n",
      "[CV] END .............C=12, coef0=0.1, gamma=0.2, kernel=rbf; total time=   4.1s\n",
      "[CV] END .............C=12, coef0=0.1, gamma=0.2, kernel=rbf; total time=   4.4s\n",
      "[CV] END .............C=12, coef0=0.1, gamma=0.2, kernel=rbf; total time=   4.2s\n",
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      "[CV] END .............C=12, coef0=0.1, gamma=0.2, kernel=rbf; total time=   4.3s\n",
      "[CV] END ..........C=12, coef0=0.1, gamma=0.3, kernel=linear; total time=  51.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ..........C=12, coef0=0.1, gamma=0.3, kernel=linear; total time= 1.1min\n",
      "[CV] END ..........C=12, coef0=0.1, gamma=0.3, kernel=linear; total time=  52.1s\n",
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      "[CV] END ..........C=13, coef0=0.1, gamma=0.1, kernel=linear; total time=  47.6s\n",
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      "[CV] END ..........C=13, coef0=0.1, gamma=0.2, kernel=linear; total time=  47.5s\n",
      "[CV] END ..........C=13, coef0=0.1, gamma=0.2, kernel=linear; total time=  46.7s\n",
      "[CV] END ..........C=13, coef0=0.1, gamma=0.2, kernel=linear; total time=  46.0s\n",
      "[CV] END ..........C=13, coef0=0.1, gamma=0.2, kernel=linear; total time=  50.1s\n",
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      "[CV] END .............C=13, coef0=0.1, gamma=0.2, kernel=rbf; total time=   3.6s\n",
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      "[CV] END .............C=13, coef0=0.1, gamma=0.2, kernel=rbf; total time=   3.7s\n",
      "[CV] END .............C=13, coef0=0.1, gamma=0.2, kernel=rbf; total time=   3.7s\n",
      "[CV] END .............C=13, coef0=0.1, gamma=0.2, kernel=rbf; total time=   4.2s\n",
      "[CV] END ..........C=13, coef0=0.1, gamma=0.3, kernel=linear; total time=  48.0s\n",
      "[CV] END ..........C=13, coef0=0.1, gamma=0.3, kernel=linear; total time=  47.1s\n",
      "[CV] END ..........C=13, coef0=0.1, gamma=0.3, kernel=linear; total time=  47.7s\n",
      "[CV] END ..........C=13, coef0=0.1, gamma=0.3, kernel=linear; total time=  49.0s\n",
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      "[CV] END .............C=13, coef0=0.1, gamma=0.3, kernel=rbf; total time=   4.5s\n",
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      "[CV] END .............C=13, coef0=0.1, gamma=0.3, kernel=rbf; total time=   3.7s\n",
      "[CV] END ..........C=13, coef0=0.1, gamma=0.4, kernel=linear; total time=  45.5s\n",
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      "[CV] END ..........C=14, coef0=0.1, gamma=0.1, kernel=linear; total time=  42.9s\n",
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      "[CV] END ..........C=14, coef0=0.1, gamma=0.1, kernel=linear; total time=  51.9s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.1, kernel=rbf; total time=   3.3s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.1, kernel=rbf; total time=   3.3s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.1, kernel=rbf; total time=   3.4s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.1, kernel=rbf; total time=   4.0s\n",
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      "[CV] END ..........C=14, coef0=0.1, gamma=0.2, kernel=linear; total time=  44.3s\n",
      "[CV] END ..........C=14, coef0=0.1, gamma=0.2, kernel=linear; total time=  51.0s\n",
      "[CV] END ..........C=14, coef0=0.1, gamma=0.2, kernel=linear; total time=  55.3s\n",
      "[CV] END ..........C=14, coef0=0.1, gamma=0.2, kernel=linear; total time=  59.2s\n",
      "[CV] END ..........C=14, coef0=0.1, gamma=0.2, kernel=linear; total time=  53.5s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.2, kernel=rbf; total time=   4.1s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.2, kernel=rbf; total time=   4.1s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.2, kernel=rbf; total time=   3.9s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.2, kernel=rbf; total time=   4.0s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.2, kernel=rbf; total time=   4.2s\n",
      "[CV] END ..........C=14, coef0=0.1, gamma=0.3, kernel=linear; total time=  45.1s\n",
      "[CV] END ..........C=14, coef0=0.1, gamma=0.3, kernel=linear; total time=  52.1s\n",
      "[CV] END ..........C=14, coef0=0.1, gamma=0.3, kernel=linear; total time=  48.5s\n",
      "[CV] END ..........C=14, coef0=0.1, gamma=0.3, kernel=linear; total time=  52.6s\n",
      "[CV] END ..........C=14, coef0=0.1, gamma=0.3, kernel=linear; total time=  50.4s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.3, kernel=rbf; total time=   4.4s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.3, kernel=rbf; total time=   4.5s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.3, kernel=rbf; total time=   4.4s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.3, kernel=rbf; total time=   4.0s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.3, kernel=rbf; total time=   3.9s\n",
      "[CV] END ..........C=14, coef0=0.1, gamma=0.4, kernel=linear; total time=  45.7s\n",
      "[CV] END ..........C=14, coef0=0.1, gamma=0.4, kernel=linear; total time=  49.8s\n",
      "[CV] END ..........C=14, coef0=0.1, gamma=0.4, kernel=linear; total time=  47.3s\n",
      "[CV] END ..........C=14, coef0=0.1, gamma=0.4, kernel=linear; total time=  51.8s\n",
      "[CV] END ..........C=14, coef0=0.1, gamma=0.4, kernel=linear; total time=  49.4s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.4, kernel=rbf; total time=   4.5s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.4, kernel=rbf; total time=   4.5s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.4, kernel=rbf; total time=   4.6s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.4, kernel=rbf; total time=   4.6s\n",
      "[CV] END .............C=14, coef0=0.1, gamma=0.4, kernel=rbf; total time=   4.4s\n",
      "mse: 110533814.85163715\n",
      "r2_score: 0.5342222405871129\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid search for best svm\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "svr_param = {\n",
    "    \"C\":range(10,15),\n",
    "    \"kernel\":[\"linear\",\"rbf\"],\n",
    "    \"gamma\":[0.1,0.2,0.3,0.4],\n",
    "    \"coef0\":[0.1]\n",
    "}\n",
    "gcv_svr = GridSearchCV(svr,svr_param,verbose=2)\n",
    "gcv_svr.fit(ss_train_x,ss_train_y.ravel())\n",
    "svr_best_pred = gcv_svr.best_estimator_.predict(ss_test_x)\n",
    "ss_svr_best = ss_y.inverse_transform(svr_best_pred)\n",
    "print(\"mse:\",mean_squared_error(ss_svr_best,y_test))\n",
    "print(\"r2_score:\",r2_score(ss_svr_best,y_test))\n",
    "plt.plot(list(range(len(ss_svr_best[:100]))),ss_svr_best[:100])\n",
    "plt.plot(list(range(len(y_test[:100]))),y_test[:100])\n",
    "plt.legend(['pred','true'])\n",
    "plt.show()\n",
    "plt.savefig(\"grid_svr.png\")\n",
    "mses.append(mean_squared_error(ss_svr_best,y_test))\n",
    "r2_scores.append(r2_score(ss_svr_best,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 14, 'coef0': 0.1, 'gamma': 0.1, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcv_svr.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the best parameters of svm regression model, parameters are {'C': 3, 'coef0': 0.1, 'gamma': 0.1, 'kernel': 'rbf'}."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best lasso\n",
    "#### Search the best svm regression model by using grid search alogrithm.\n",
    "#### R2 score and mean squared score  are used to measure the performance of the best model of lasso. \n",
    "#### Select out the first 100 test labels and predict labels for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n",
      "[CV] END .........................................alpha=0.01; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.01; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.01; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.01; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.01; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.02; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.02; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.02; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.02; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.02; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.03; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.03; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.03; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.03; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.03; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.04; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.04; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.04; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.04; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.04; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.05; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.05; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.05; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.05; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.05; total time=   0.0s\n",
      "[CV] END .........................alpha=0.060000000000000005; total time=   0.0s\n",
      "[CV] END .........................alpha=0.060000000000000005; total time=   0.0s\n",
      "[CV] END .........................alpha=0.060000000000000005; total time=   0.0s\n",
      "[CV] END .........................alpha=0.060000000000000005; total time=   0.0s\n",
      "[CV] END .........................alpha=0.060000000000000005; total time=   0.0s\n",
      "[CV] END ..........................alpha=0.06999999999999999; total time=   0.0s\n",
      "[CV] END ..........................alpha=0.06999999999999999; total time=   0.0s\n",
      "[CV] END ..........................alpha=0.06999999999999999; total time=   0.0s\n",
      "[CV] END ..........................alpha=0.06999999999999999; total time=   0.0s\n",
      "[CV] END ..........................alpha=0.06999999999999999; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.08; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.08; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.08; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.08; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.08; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.09; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.09; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.09; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.09; total time=   0.0s\n",
      "[CV] END .........................................alpha=0.09; total time=   0.0s\n",
      "mse: 167820930.78951946\n",
      "r2_score: 0.36308391162688425\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid search for best lasso\n",
    "la_param = {\n",
    "    \"alpha\":list(np.arange(0.01,0.1,0.01))\n",
    "}\n",
    "gcv_la = GridSearchCV(la, la_param,verbose=2)\n",
    "gcv_la.fit(ss_train_x,ss_train_y.ravel())\n",
    "la_best_pred = gcv_la.best_estimator_.predict(ss_test_x)\n",
    "ss_la_best = ss_y.inverse_transform(la_best_pred)\n",
    "print(\"mse:\",mean_squared_error(ss_la_best,y_test))\n",
    "print(\"r2_score:\",r2_score(ss_la_best,y_test))\n",
    "mean_squared_error(ss_la_best, y_test)\n",
    "plt.plot(list(range(len(ss_la_best[:100]))),ss_la_best[:100])\n",
    "plt.plot(list(range(len(y_test[:100]))),y_test[:100])\n",
    "plt.legend(['pred','true'])\n",
    "plt.show()\n",
    "plt.savefig(\"grid_la.png\")\n",
    "mses.append(mean_squared_error(ss_la_best,y_test))\n",
    "r2_scores.append(r2_score(ss_la_best,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.01}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcv_la.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the best parameter of lasso regression model, the parameter is {'alpha': 0.01}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best elastic net\n",
    "#### Search the best svm regression model by using grid search alogrithm.\n",
    "#### R2 score and mean squared score  are used to measure the performance of the best model of elastic net. \n",
    "#### Select out the first 100 test labels and predict labels for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n",
      "[CV] END ..........................................alpha=0.1; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.1; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.1; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.1; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.1; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.2; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.2; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.2; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.2; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.2; total time=   0.0s\n",
      "[CV] END ..........................alpha=0.30000000000000004; total time=   0.0s\n",
      "[CV] END ..........................alpha=0.30000000000000004; total time=   0.0s\n",
      "[CV] END ..........................alpha=0.30000000000000004; total time=   0.0s\n",
      "[CV] END ..........................alpha=0.30000000000000004; total time=   0.0s\n",
      "[CV] END ..........................alpha=0.30000000000000004; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.4; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.4; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.4; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.4; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.4; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.5; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.5; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.5; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.5; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.5; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.6; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.6; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.6; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.6; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.6; total time=   0.0s\n",
      "[CV] END ...........................alpha=0.7000000000000001; total time=   0.0s\n",
      "[CV] END ...........................alpha=0.7000000000000001; total time=   0.0s\n",
      "[CV] END ...........................alpha=0.7000000000000001; total time=   0.0s\n",
      "[CV] END ...........................alpha=0.7000000000000001; total time=   0.0s\n",
      "[CV] END ...........................alpha=0.7000000000000001; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.8; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.8; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.8; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.8; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.8; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.9; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.9; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.9; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.9; total time=   0.0s\n",
      "[CV] END ..........................................alpha=0.9; total time=   0.0s\n",
      "mse: 164088814.8047257\n",
      "r2_score: 0.11825378254056684\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid search for best elastic net\n",
    "en_param = {\n",
    "    \"alpha\":list(np.arange(0.1,1,0.1))\n",
    "}\n",
    "gcv_en = GridSearchCV(en, en_param,verbose=2)\n",
    "gcv_en.fit(ss_train_x,ss_train_y.ravel())\n",
    "en_best_pred = gcv_en.best_estimator_.predict(ss_test_x)\n",
    "ss_en_best = ss_y.inverse_transform(en_best_pred)\n",
    "print(\"mse:\",mean_squared_error(ss_en_best,y_test))\n",
    "print(\"r2_score:\",r2_score(ss_en_best,y_test))\n",
    "mean_squared_error(ss_en_best, y_test)\n",
    "plt.plot(list(range(len(ss_en_best[:100]))),ss_en_best[:100])\n",
    "plt.plot(list(range(len(y_test[:100]))),y_test[:100])\n",
    "plt.legend(['pred','true'])\n",
    "plt.show()\n",
    "plt.savefig(\"grid_en.png\")\n",
    "mses.append(mean_squared_error(ss_en_best,y_test))\n",
    "r2_scores.append(r2_score(ss_en_best,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.1}"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcv_en.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the best parameter of elastic net regression, parameter is {'alpha': 0.1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = ['linear','svm','lasso','elastic net','best svm','best lasso', 'best elastic net']\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.title('MSE of Models')\n",
    "plt.ylabel('MSE value')\n",
    "plt.xlabel('Model')\n",
    "plt.plot(models, mses)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In terms of mean squared error, the tuned svm regression value is lowest, that means tuned svm regression model has high performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure(figsize=(10,6))\n",
    "plt.title('R2 Score of Models')\n",
    "plt.ylabel('R2 Score')\n",
    "plt.xlabel('Model')\n",
    "plt.plot(models, r2_scores)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In terms of mean r2 score, the tuned svm regression is highest, that means tuned svm regression model has high performance. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
